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LLM-in-Vision

Recent LLM (Large Language Models)-based CV and multi-modal works. Welcome to comment/contribute!

2024.6

  • (arXiv 2024.6) Bootstrap3D: Improving 3D Content Creation with Synthetic Data, [Paper], [Project]

2024.5

  • (arXiv 2024.5) VideoOFA: Two-Stage Pre-Training for Video-to-Text Generation, [Paper]

  • (arXiv 2024.5) Grounded 3D-LLM with Referent Tokens, [Paper], [Project]

  • (arXiv 2024.5) Self-supervised Pre-training for Transferable Multi-modal Perception, [Paper]

  • (arXiv 2024.5) Multi-modal Generation via Cross-Modal In-Context Learning, [Paper]

  • (arXiv 2024.5) RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots, [Paper], [Project]

  • (arXiv 2024.5) Unveiling the Tapestry of Consistency in Large Vision-Language Models, [Paper]

  • (arXiv 2024.5) Dense Connector for MLLMs, [Paper], [Project]

  • (arXiv 2024.5) Adapting Multi-modal Large Language Model to Concept Drift in the Long-tailed Open World, [Paper], [Project]

  • (arXiv 2024.5) VTG-LLM: INTEGRATING TIMESTAMP KNOWLEDGE INTO VIDEO LLMS FOR ENHANCED VIDEO TEMPORAL GROUNDING, [Paper], [Project]

  • (arXiv 2024.5) Calibrated Self-Rewarding Vision Language Models, [Paper], [Project]

  • (arXiv 2024.5) From Text to Pixel: Advancing Long-Context Understanding in MLLMs, [Paper], [Project]

  • (arXiv 2024.5) Explaining Multi-modal Large Language Models by Analyzing their Vision Perception, [Paper]

  • (arXiv 2024.5) Octopi: Object Property Reasoning with Large Tactile-Language Models, [Paper], [Project]

  • (arXiv 2024.5) Auto-Encoding Morph-Tokens for Multimodal LLM, [Paper], [Project]

  • (arXiv 2024.5) What matters when building vision-language models? [Paper]

2024.4

  • (arXiv 2024.4) VITRON: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing, [Paper], [Project]

  • (arXiv 2024.4) GROUNDHOG: Grounding Large Language Models to Holistic Segmentation, [Paper], [Project]

  • (arXiv 2024.4) Hallucination of Multimodal Large Language Models: A Survey, [Paper], [Project]

  • (arXiv 2024.4) PLLaVA: Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning, [Paper], [Project]

  • (arXiv 2024.4) MovieChat+: Question-aware Sparse Memory for Long Video Question Answering, [Paper], [Project]

  • (arXiv 2024.4) Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models, [Paper], [Project]

  • (arXiv 2024.4) A Survey on the Memory Mechanism of Large Language Model based Agents, [Paper]

  • (arXiv 2024.4) Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples, [Paper]

  • (arXiv 2024.4) A Multimodal Automated Interpretability Agent, [Paper], [Project]

  • (arXiv 2024.4) Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models, [Paper], [Project]

  • (arXiv 2024.4) TextSquare: Scaling up Text-Centric Visual Instruction Tuning, [Paper]

  • (arXiv 2024.4) What Makes Multimodal In-Context Learning Work?, [Paper]

  • (arXiv 2024.4) ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction, [Paper], [Project]

  • (arXiv 2024.4) Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs, [Paper]

  • (arXiv 2024.4) Seeing Beyond Classes: Zero-Shot Grounded Situation Recognition via Language Explainer, [Paper]

  • (arXiv 2024.4) MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI, [Paper]

  • (arXiv 2024.4) Cantor: Inspiring Multimodal Chain-of-Thought of MLLM, [Paper], [Project]

  • (arXiv 2024.4) Make-it-Real: Unleashing Large Multimodal Model’s Ability for Painting 3D Objects with Realistic Materials, [Paper], [Project]

  • (arXiv 2024.4) How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites, [Paper], [Project]

  • (arXiv 2024.4) Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings, [Paper], [Project]

  • (arXiv 2024.4) SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension, [Paper], [Project]

  • (arXiv 2024.4) List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs, [Paper], [Project]

  • (arXiv 2024.4) Step Differences in Instructional Video, [Paper]

  • (arXiv 2024.4) A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming, [Paper]

  • (arXiv 2024.4) TextSquare: Scaling up Text-Centric Visual Instruction Tuning, [Paper]

  • (arXiv 2024.4) Pre-trained Vision-Language Models Learn Discoverable Visual Concepts, [Paper], [Project]

  • (arXiv 2024.4) MoVA: Adapting Mixture of Vision Experts to Multimodal Context, [Paper], [Project]

  • (arXiv 2024.4) Uni3DR^2: Unified Scene Representation and Reconstruction for 3D Large Language Models, [Paper], [Project]

  • (arXiv 2024.4) Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models, [Paper], [Project]

  • (arXiv 2024.4) Eyes Can Deceive: Benchmarking Counterfactual Reasoning Capabilities of Multimodal Large Language Models, [Paper]

  • (arXiv 2024.4) Empowering Large Language Models on Robotic Manipulation with Affordance Prompting, [Paper]

  • (arXiv 2024.4) Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning, [Paper]

  • (arXiv 2024.4) OVAL-Prompt: Open-Vocabulary Affordance Localization for Robot Manipulation through LLM Affordance-Grounding, [Paper]

  • (arXiv 2024.4) FoundationGrasp: Generalizable Task-Oriented Grasping with Foundation Models, [Paper], [Project]

  • (arXiv 2024.4) Towards Human Awareness in Robot Task Planning with Large Language Models, [Paper]

  • (arXiv 2024.4) Self-Supervised Visual Preference Alignment, [Paper]

  • (arXiv 2024.4) Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering, [Paper]

  • (arXiv 2024.4) COMBO: Compositional World Models for Embodied Multi-Agent Cooperation, [Paper], [Project]

  • (arXiv 2024.4) Octopus v3: Technical Report for On-device Sub-billion Multimodal AI Agent, [Paper]

  • (arXiv 2024.4) Fact:Teaching MLLMs with Faithful, Concise and Transferable Rationales, [Paper]

  • (arXiv 2024.4) Exploring the Transferability of Visual Prompting for Multimodal Large Language Models, [Paper]

  • (arXiv 2024.4) TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal Large Language Models, [Paper], [Project]

  • (arXiv 2024.4) EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM, [Paper]

  • (arXiv 2024.4) BRIDGING VISION AND LANGUAGE SPACES WITH ASSIGNMENT PREDICTION, [Paper]

  • (arXiv 2024.4) TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding, [Paper], [Project]

  • (arXiv 2024.4) HOI-Ref: Hand-Object Interaction Referral in Egocentric Vision, [Paper], [Project]

  • (arXiv 2024.4) MMInA: Benchmarking Multihop Multimodal Internet Agents, [Paper], [Project]

  • (arXiv 2024.4) Evolving Interpretable Visual Classifiers with Large Language Models, [Paper]

  • (arXiv 2024.4) OSWORLD: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments, [Paper], [Project]

  • (arXiv 2024.4) Reflectance Estimation for Proximity Sensing by Vision-Language Models: Utilizing Distributional Semantics for Low-Level Cognition in Robotics, [Paper]

  • (arXiv 2024.4) Sketch-Plan-Generalize: Continual Few-Shot Learning of Inductively Generalizable Spatial Concepts for Language-Guided Robot Manipulation, [Paper]

  • (arXiv 2024.4) MORPHeus: a Multimodal One-armed Robot-assisted Peeling System with Human Users In-the-loop, [Paper], [Project]

  • (arXiv 2024.4) GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks, [Paper], [Project]

  • (arXiv 2024.4) Exploring the Potential of Large Foundation Models for Open-Vocabulary HOI Detection, [Paper], [Project]

2024.3

  • (arXiv 2024.3) AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling, [Paper], [Project]

  • (arXiv 2024.3) OCTAVIUS: MITIGATING TASK INTERFERENCE IN MLLMS VIA LORA-MOE, [Paper], [Project]

  • (arXiv 2024.3) INSTRUCTCV: INSTRUCTION-TUNED TEXT-TO-IMAGE DIFFUSION MODELS AS VISION GENERALISTS, [Paper], [Project]

  • (arXiv 2024.3) Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld, [Paper], [Project]

  • (arXiv 2024.3) ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models, [Paper], [Project]

  • (arXiv 2024.3) MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control, [Paper], [Project]

  • (arXiv 2024.3) LEGO: Learning EGOcentric Action Frame Generation via Visual Instruction Tuning, [Paper], [Project]

  • (arXiv 2024.3) RAIL: Robot Affordance Imagination with Large Language Models, [Paper]

  • (arXiv 2024.3) Are We on the Right Way for Evaluating Large Vision-Language Models? [Paper], [Project]

  • (arXiv 2024.3) FSMR: A Feature Swapping Multi-modal Reasoning Approach with Joint Textual and Visual Clues, [Paper], [Project]

  • (arXiv 2024.3) Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models, [Paper], [Project]

  • (arXiv 2024.3) OAKINK2 : A Dataset of Bimanual Hands-Object Manipulation in Complex Task Completion, [Paper], [Project]

  • (arXiv 2024.3) InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction, [Paper], [Project]

  • (arXiv 2024.3) MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training, [Paper]

  • (arXiv 2024.3) Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models, [Paper], [Project]

  • (arXiv 2024.3) INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations, [Paper]

  • (arXiv 2024.3) DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM, [Paper]

  • (arXiv 2024.3) Embodied LLM Agents Learn to Cooperate in Organized Teams, [Paper]

  • (arXiv 2024.3) To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions, [Paper], [Project]

  • (arXiv 2024.3) BTGenBot: Behavior Tree Generation for Robotic Tasks with Lightweight LLMs, [Paper]

  • (arXiv 2024.3) Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models, [Paper], [Project]

  • (arXiv 2024.3) HYDRA: A Hyper Agent for Dynamic Compositional Visual Reasoning, [Paper], [Project]

  • (arXiv 2024.3) RelationVLM: Making Large Vision-Language Models Understand Visual Relations, [Paper]

  • (arXiv 2024.3) Towards Multimodal In-Context Learning for Vision & Language Models, [Paper]

  • (arXiv 2024.3) Images are Achilles’ Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models, [Paper]

  • (arXiv 2024.3) HawkEye: Training Video-Text LLMs for Grounding Text in Videos, [Paper], [Project]

  • (arXiv 2024.3) UniCode: Learning a Unified Codebook for Multimodal Large Language Models, [Paper]

  • (arXiv 2024.3) Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models, [Paper], [Project]

  • (arXiv 2024.3) MoAI: Mixture of All Intelligence for Large Language and Vision Models, [Paper], [Project]

  • (arXiv 2024.3) Multi-modal Auto-regressive Modeling via Visual Words, [Paper], [Project]

  • (arXiv 2024.3) DeepSeek-VL: Towards Real-World Vision-Language Understanding, [Paper], [Project]

  • (arXiv 2024.3) WILL GPT-4 RUN DOOM?, [Paper], [Project]

  • (arXiv 2024.3) Debiasing Large Visual Language Models, [Paper], [Project]

2024.2

  • (arXiv 2024.2) Efficient Multimodal Learning from Data-centric Perspective, [Paper], [Project]

  • (arXiv 2024.2) Dej´ a Vu Memorization in Vision-Language Models, [Paper]

  • (arXiv 2024.2) Lumos: Empowering Multimodal LLMs with Scene Text Recognition, [Paper]

  • (arXiv 2024.2) MOSAIC: A Modular System for Assistive and Interactive Cooking, [Paper], [Project]

  • (arXiv 2024.2) Visual Hallucinations of Multi-modal Large Language Models, [Paper], [Project]

  • (arXiv 2024.2) DualFocus: Integrating Macro and Micro Perspectives in Multi-modal Large Language Models, [Paper], [Project]

  • (arXiv 2024.2) RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation, [Paper]

  • (arXiv 2024.2) TinyLLaVA: A Framework of Small-scale Large Multimodal Models, [Paper], [Project]

  • (arXiv 2024.2) Enhancing Robotic Manipulation with AI Feedback from Multimodal Large Language Models, [Paper]

  • (arXiv 2024.2) Uncertainty-Aware Evaluation for Vision-Language Models, [Paper], [Project]

  • (arXiv 2024.2) RealDex: Towards Human-like Grasping for Robotic Dexterous Hand, [Paper]

  • (arXiv 2024.2) Aligning Modalities in Vision Large Language Models via Preference Fine-tuning, [Paper], [Project]

  • (arXiv 2024.2) Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships, [Paper]

  • (arXiv 2024.2) ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning, [Paper], [Project]

  • (arXiv 2024.2) LVCHAT: Facilitating Long Video Comprehension, [Paper], [Project]

  • (arXiv 2024.2) Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models, [Paper], [Project]

  • (arXiv 2024.2) Using Left and Right Brains Together: Towards Vision and Language Planning, [Paper]

  • (arXiv 2024.2) Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering, [Paper]

  • (arXiv 2024.2) PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter, [Paper]

  • (arXiv 2024.2) Grounding LLMs For Robot Task Planning Using Closed-loop State Feedback, [Paper]

  • (arXiv 2024.2) BBSEA: An Exploration of Brain-Body Synchronization for Embodied Agents, [Paper], [Project]

  • (arXiv 2024.2) Reasoning Grasping via Multimodal Large Language Model, [Paper]

  • (arXiv 2024.2) LOTA-BENCH: BENCHMARKING LANGUAGE-ORIENTED TASK PLANNERS FOR EMBODIED AGENTS, [Paper], [Project]

  • (arXiv 2024.2) OS-COPILOT: TOWARDS GENERALIST COMPUTER AGENTS WITH SELF-IMPROVEMENT, [Paper], [Project]

  • (arXiv 2024.2) Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning, [Paper]

  • (arXiv 2024.2) Preference-Conditioned Language-Guided Abstraction, [Paper]

  • (arXiv 2024.2) Affordable Generative Agents, [Paper], [Project]

  • (arXiv 2024.2) An Interactive Agent Foundation Model, [Paper]

  • (arXiv 2024.2) InCoRo: In-Context Learning for Robotics Control with Feedback Loops, [Paper]

  • (arXiv 2024.2) Real-World Robot Applications of Foundation Models: A Review, [Paper]

  • (arXiv 2024.2) Question Aware Vision Transformer for Multimodal Reasoning, [Paper]

  • (arXiv 2024.2) SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models, [Paper], [Project]

  • (arXiv 2024.2) CREMA: Multimodal Compositional Video Reasoning via Efficient Modular Adaptation and Fusion, [Paper], [Project]

  • (arXiv 2024.2) S-AGENTS: SELF-ORGANIZING AGENTS IN OPENENDED ENVIRONMENT, [Paper], [Project]

  • (arXiv 2024.2) Code as Reward: Empowering Reinforcement Learning with VLMs, [Paper]

  • (arXiv 2024.2) Data-efficient Large Vision Models through Sequential Autoregression, [Paper], [Project]

  • (arXiv 2024.2) MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark, [Paper], [Project]

  • (arXiv 2024.2) Beyond Lines and Circles: Unveiling the Geometric Reasoning Gap in Large Language Models, [Paper], [Project]

  • (arXiv 2024.2) “Task Success” is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors, [Paper], [Project]

  • (arXiv 2024.2) RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents, [Paper]

  • (arXiv 2024.2) Uni3D-LLM: Unifying Point Cloud Perception, Generation and Editing with Large Language Models, [Paper]

  • (arXiv 2024.2) The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs, [Paper], [Project]

  • (arXiv 2024.2) MobileVLM V2: Faster and Stronger Baseline for Vision Language Model, [Paper], [Project]

  • (arXiv 2024.2) CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations, [Paper], [Project]

  • (arXiv 2024.2) Compositional Generative Modeling: A Single Model is Not All You Need, [Paper]

  • (arXiv 2024.2) IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition, [Paper]

  • (arXiv 2024.2) SKIP \N: A SIMPLE METHOD TO REDUCE HALLUCINATION IN LARGE VISION-LANGUAGE MODELS, [Paper], [Project]

2024.1

  • (arXiv 2024.1) Red Teaming Visual Language Models, [Paper], [Project]

  • (arXiv 2024.1) AUTORT: EMBODIED FOUNDATION MODELS FOR LARGE SCALE ORCHESTRATION OF ROBOTIC AGENTS, [Paper], [Project]

  • (arXiv 2024.1) LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model, [Paper]

  • (arXiv 2024.1) TRAINING DIFFUSION MODELS WITH REINFORCEMENT LEARNING, [Paper], [Project]

  • (arXiv 2024.1) SWARMBRAIN: EMBODIED AGENT FOR REAL-TIME STRATEGY GAME STARCRAFT II VIA LARGE LANGUAGE MODELS, [Paper]

  • (arXiv 2024.1) YTCommentQA: Video Question Answerability in Instructional Videos, [Paper], [Project]

  • (arXiv 2024.1) MouSi: Poly-Visual-Expert Vision-Language Models, [Paper], [Project]

  • (arXiv 2024.1) DORAEMONGPT: TOWARD UNDERSTANDING DYNAMIC SCENES WITH LARGE LANGUAGE MODELS, [Paper], [Project]

  • (arXiv 2024.1) KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning, [Paper]

  • (arXiv 2024.1) Growing from Exploration: A self-exploring framework for robots based on foundation models, [Paper], [Project]

  • (arXiv 2024.1) TRUE KNOWLEDGE COMES FROM PRACTICE: ALIGNING LLMS WITH EMBODIED ENVIRONMENTS VIA REINFORCEMENT LEARNING, [Paper], [Project]

  • (arXiv 2024.1) Red Teaming Visual Language Models, [Paper], [Project]

  • (arXiv 2024.1) The Neglected Tails of Vision-Language Models, [Paper], [Project]

  • (arXiv 2024.1) Zero Shot Open-ended Video Inference, [Paper]

  • (arXiv 2024.1) Small Language Model Meets with Reinforced Vision Vocabulary, [Paper], [Project]

  • (arXiv 2024.1) HAZARD CHALLENGE: EMBODIED DECISION MAKING IN DYNAMICALLY CHANGING ENVIRONMENTS, [Paper], [Project]

  • (arXiv 2024.1) VisualWebArena: EVALUATING MULTIMODAL AGENTS ON REALISTIC VISUAL WEB TASKS, [Paper], [Project]

  • (arXiv 2024.1) ChatterBox: Multi-round Multimodal Referring and Grounding, [Paper], [Project]

  • (arXiv 2024.1) CONTEXTUAL: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models, [Paper], [Project]

  • (arXiv 2024.1) UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion, [Paper], [Project]

  • (arXiv 2024.1) DEMOCRATIZING FINE-GRAINED VISUAL RECOGNITION WITH LARGE LANGUAGE MODELS, [Paper], [Project]

  • (arXiv 2024.1) Benchmarking Large Multimodal Models against Common Corruptions, [Paper], [Project]

  • (arXiv 2024.1) CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark, [Paper], [Project]

  • (arXiv 2024.1) Prompting Large Vision-Language Models for Compositional Reasoning, [Paper], [Project]

  • (arXiv 2024.1) Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs, [Paper], [Project]

  • (arXiv 2024.1) SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities, [Paper], [Project]

  • (arXiv 2024.1) Towards A Better Metric for Text-to-Video Generation, [Paper], [Project]

  • (arXiv 2024.1) EXPLOITING GPT-4 VISION FOR ZERO-SHOT POINT CLOUD UNDERSTANDING, [Paper]

  • (arXiv 2024.1) MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception, [Paper], [Project]

  • (arXiv 2024.1) GATS: Gather-Attend-Scatter, [Paper]

  • (arXiv 2024.1) DiffusionGPT: LLM-Driven Text-to-Image Generation System, [Paper], [Project]

  • (arXiv 2024.1) TEMPORAL INSIGHT ENHANCEMENT: MITIGATING TEMPORAL HALLUCINATION IN MULTIMODAL LARGE LANGUAGE MODELS, [Paper]

  • (arXiv 2024.1) Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation, [Paper]

  • (arXiv 2024.1) GPT4Ego: Unleashing the Potential of Pre-trained Models for Zero-Shot Egocentric Action Recognition, [Paper]

  • (arXiv 2024.1) SCENEVERSE: Scaling 3D Vision-Language Learning for Grounded Scene Understanding, [Paper], [Project]

  • (arXiv 2024.1) Vlogger: Make Your Dream A Vlog, [Paper], [Project]

  • (arXiv 2024.1) CognitiveDog: Large Multimodal Model Based System to Translate Vision and Language into Action of Quadruped Robot, [Paper], [Project]

  • (arXiv 2024.1) Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability, [Paper]

  • (arXiv 2024.1) Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences, [Paper], [Project]

  • (arXiv 2024.1) Weakly Supervised Gaussian Contrastive Grounding with Large Multimodal Models for Video Question Answering, [Paper]

  • (arXiv 2024.1) Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World Knowledge, [Paper]

  • (arXiv 2024.1) Tool-LMM: A Large Multi-Modal Model for Tool Agent Learning, [Paper], [Project]

  • (arXiv 2024.1) MMToM-QA: Multimodal Theory of Mind Question Answering, [Paper], [Project]

  • (arXiv 2024.1) EgoGen: An Egocentric Synthetic Data Generator, [Paper], [Project]

  • (arXiv 2024.1) COCO IS “ALL” YOU NEED FOR VISUAL INSTRUCTION FINE-TUNING, [Paper]

  • (arXiv 2024.1) OCTO+: A Suite for Automatic Open-Vocabulary Object Placement in Mixed Reality, [Paper], [Project]

  • (arXiv 2024.1) MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World, [Paper], [Project]

  • (arXiv 2024.1) SELF-IMAGINE: EFFECTIVE UNIMODAL REASONING WITH MULTIMODAL MODELS USING SELF-IMAGINATION, [Paper], [Project]

  • (arXiv 2024.1) Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation, [Paper], [Project]

  • (arXiv 2024.1) Towards Language-Driven Video Inpainting via Multimodal Large Language Models, [Paper], [Project]

  • (arXiv 2024.1) An Improved Baseline for Reasoning Segmentation with Large Language Model, [Paper]

  • (arXiv 2024.1) MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World, [Paper], [Project]

  • (arXiv 2024.1) 3D-PREMISE: CAN LARGE LANGUAGE MODELS GENERATE 3D SHAPES WITH SHARP FEATURES AND PARAMETRIC CONTROL? [Paper]

  • (arXiv 2024.1) 360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model, [Paper], [Project]

  • (arXiv 2024.1) Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs, [Paper], [Project]

  • (arXiv 2024.1) AffordanceLLM: Grounding Affordance from Vision Language Models, [Paper], [Project]

  • (arXiv 2024.1) ModaVerse: Efficiently Transforming Modalities with LLMs, [Paper]

  • (arXiv 2024.1) REPLAN: ROBOTIC REPLANNING WITH PERCEPTION AND LANGUAGE MODELS, [Paper], [Project]

  • (arXiv 2024.1) Language-Conditioned Robotic Manipulation with Fast and Slow Thinking, [Paper], [Project]

  • (arXiv 2024.1) FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild, [Paper]

  • (arXiv 2024.1) REBUS: A Robust Evaluation Benchmark of Understanding Symbols, [Paper], [Project]

  • (arXiv 2024.1) LEGO:Language Enhanced Multi-modal Grounding Model, [Paper], [Project]

  • (arXiv 2024.1) Distilling Vision-Language Models on Millions of Videos, [Paper]

  • (arXiv 2024.1) EXPLORING LARGE LANGUAGE MODEL BASED INTELLIGENT AGENTS: DEFINITIONS, METHODS, AND PROSPECTS, [Paper]

  • (arXiv 2024.1) AGENT AI: SURVEYING THE HORIZONS OF MULTIMODAL INTERACTION, [Paper]

  • (arXiv 2024.1) ExTraCT – Explainable Trajectory Corrections from language inputs using Textual description of features, [Paper]

  • (arXiv 2024.1) Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models, [Paper]

  • (arXiv 2024.1) GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation, [Paper], [Project]

  • (arXiv 2024.1) Large Language Models as Visual Cross-Domain Learners, [Paper], [Project]

  • (arXiv 2024.1) 3DMIT: 3D MULTI-MODAL INSTRUCTION TUNING FOR SCENE UNDERSTANDING, [Paper], [Project]

  • (arXiv 2024.1) CaMML: Context-Aware Multimodal Learner for Large Models, [Paper]

  • (arXiv 2024.1) Object-Centric Instruction Augmentation for Robotic Manipulation, [Paper], [Project]

  • (arXiv 2024.1) Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers, [Paper]

  • (arXiv 2024.1) A Vision Check-up for Language Models, [Paper], [Project]

  • (arXiv 2024.1) GPT-4V(ision) is a Generalist Web Agent, if Grounded, [Paper], [Project]

  • (arXiv 2024.1) LLaVA-ϕ: Efficient Multi-Modal Assistant with Small Language Model, [Paper], [Project]

2023.12

  • (arXiv 2023.12) GLaMM: Pixel Grounding Large Multimodal Model, [Paper], [Project]

  • (arXiv 2023.12) MOCHa: Multi-Objective Reinforcement Mitigating Caption Hallucinations, [Paper], [Project]

  • (arXiv 2023.12) Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models, [Paper], [Project]

  • (arXiv 2023.12) Customization Assistant for Text-to-image Generation, [Paper]

  • (arXiv 2023.12) GPT4Point: A Unified Framework for Point-Language Understanding and Generation, [Paper], [Project]

  • (arXiv 2023.12) LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models, [Paper], [Project]

  • (arXiv 2023.12) BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models, [Paper], [Project]

  • (arXiv 2023.12) Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions, [Paper], [Project]

  • (arXiv 2023.12) Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning, [Paper], [Project]

  • (arXiv 2023.12) ETC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model, [Paper]

  • (arXiv 2023.12) Lenna: Language Enhanced Reasoning Detection Assistant, [Paper], [Project]

  • (arXiv 2023.12) VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding, [Paper]

  • (arXiv 2023.12) StoryGPT-V: Large Language Models as Consistent Story Visualizers, [Paper], [Project]

  • (arXiv 2023.12) Diversify, Don’t Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images, [Paper]

  • (arXiv 2023.12) Recursive Visual Programming, [Paper]

  • (arXiv 2023.12) PixelLM: Pixel Reasoning with Large Multimodal Model, [Paper]

  • (arXiv 2023.12) Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition, [Paper]

  • (arXiv 2023.12) Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models, [Paper], [Project]

  • (arXiv 2023.12) Video Summarization: Towards Entity-Aware Captions, [Paper]

  • (arXiv 2023.12) VLAP: Efficient Video-Language Alignment via Frame Prompting and Distilling for Video Question Answering, [Paper]

  • (arXiv 2023.12) See, Say, and Segment: Teaching LMMs to Overcome False Premises, [Paper], [Project]

  • (arXiv 2023.12) Chat-3D v2: Bridging 3D Scene and Large Language Models with Object Identifiers, [Paper], [Code]

  • (arXiv 2023.12) Interfacing Foundation Models’ Embeddings, [Paper], [Project]

  • (arXiv 2023.12) VILA: On Pre-training for Visual Language Models, [Paper]

  • (arXiv 2023.12) MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception, [Paper], [Project]

  • (arXiv 2023.12) Hallucination Augmented Contrastive Learning for Multimodal Large Language Model, [Paper]

  • (arXiv 2023.12) Honeybee: Locality-enhanced Projector for Multimodal LLM, [Paper], [Project]

  • (arXiv 2023.12) SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models, [Paper], [Project]

  • (arXiv 2023.12) InstructAny2Pix: Flexible Visual Editing via Multimodal Instruction Following, [Paper], [Project]

  • (arXiv 2023.12) EgoPlan-Bench: Benchmarking Egocentric Embodied Planning with Multimodal Large Language Models, [Paper], [Project]

  • (arXiv 2023.12) AM-RADIO: Agglomerative Model – Reduce All Domains Into One, [Paper], [Project]

  • (arXiv 2023.12) Leveraging Generative Language Models for Weakly Supervised Sentence Component Analysis in Video-Language Joint Learning, [Paper]

  • (arXiv 2023.12) How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation, [Paper], [Project]

  • (arXiv 2023.12) Audio-Visual LLM for Video Understanding, [Paper]

  • (arXiv 2023.12) AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes, [Paper], [Project]

  • (arXiv 2023.12) Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models, [Paper], [[Project]]( https:// github.com/Vill-Lab/2024-AAAI-HPT)

  • (arXiv 2023.12) Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models, [Paper], [Project]

  • (arXiv 2023.12) AllSpark: a multimodal spatiotemporal general model, [Paper]

  • (arXiv 2023.12) Tracking with Human-Intent Reasoning, [Paper], [Project]

  • (arXiv 2023.12) Retrieval-Augmented Egocentric Video Captioning, [Paper]

  • (arXiv 2023.12) COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training, [Paper], [Project]

  • (arXiv 2023.12) LARP: LANGUAGE-AGENT ROLE PLAY FOR OPEN-WORLD GAMES, [Paper], [Project]

  • (arXiv 2023.12) CLOVA: A Closed-LOop Visual Assistant with Tool Usage and Update, [Paper], [Project]

  • (arXiv 2023.12) DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics, [Paper], [Project]

  • (arXiv 2023.12) VISTA-LLAMA: Reliable Video Narrator via Equal Distance to Visual Tokens, [Paper], [Project]

  • (arXiv 2023.12) VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation, [Paper], [Project]

  • (arXiv 2023.12) Pixel Aligned Language Models, [Paper], [Project]

  • (arXiv 2023.12) Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos, [Paper]

  • (arXiv 2023.12) Q-ALIGN: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels, [Paper], [Project]

  • (arXiv 2023.12) Osprey: Pixel Understanding with Visual Instruction Tuning, [Paper], [Project]

  • (arXiv 2023.12) Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action, [Paper], [Project]

  • (arXiv 2023.12) A Simple LLM Framework for Long-Range Video Question-Answering, [Paper], [Project]

  • (arXiv 2023.12) TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones, [Paper], [Project]

  • (arXiv 2023.12) ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation, [Paper], [Project]

  • (arXiv 2023.12) ChartBench: A Benchmark for Complex Visual Reasoning in Charts, [Paper]

  • (arXiv 2023.12) FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects, [Paper], [Project]

  • (arXiv 2023.12) Make-A-Character: High Quality Text-to-3D Character Generation within Minutes, [Paper], [Project]

  • (arXiv 2023.12) Osprey: Pixel Understanding with Visual Instruction Tuning, [Paper], [Project]

  • (arXiv 2023.12) 3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V, [Paper]

  • (arXiv 2023.12) SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models, [Paper], [Project]

  • (arXiv 2023.12) VideoPoet: A Large Language Model for Zero-Shot Video Generation, [Paper], [Project]

  • (arXiv 2023.12) V∗: Guided Visual Search as a Core Mechanism in Multimodal LLMs, [Paper], [Project]

  • (arXiv 2023.12) A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties, [Paper], [Project]

  • (arXiv 2023.12) AppAgent: Multimodal Agents as Smartphone Users, [Paper], [Project]

  • (arXiv 2023.12) InfoVisDial: An Informative Visual Dialogue Dataset by Bridging Large Multimodal and Language Models, [Paper], [Project]

  • (arXiv 2023.12) Not All Steps are Equal: Efficient Generation with Progressive Diffusion Models, [Paper]

  • (arXiv 2023.12) Generative Multimodal Models are In-Context Learners, [Paper], [Project]

  • (arXiv 2023.12) VCoder: Versatile Vision Encoders for Multimodal Large Language Models, [Paper], [Project]

  • (arXiv 2023.12) LLM4VG: Large Language Models Evaluation for Video Grounding, [Paper]

  • (arXiv 2023.12) InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks, [Paper], [Project]

  • (arXiv 2023.12) VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation, [Paper], [Project]

  • (arXiv 2023.12) Plan, Posture and Go: Towards Open-World Text-to-Motion Generation, [Paper], [Project]

  • (arXiv 2023.12) MotionScript: Natural Language Descriptions for Expressive 3D Human Motions, [Paper], [Project]

  • (arXiv 2023.12) Assessing GPT4-V on Structured Reasoning Tasks, [Paper], [Project]

  • (arXiv 2023.12) Iterative Motion Editing with Natural Language, [Paper], [Project]

  • (arXiv 2023.12) Gemini: A Family of Highly Capable Multimodal Models, [Paper], [Project]

  • (arXiv 2023.12) StarVector: Generating Scalable Vector Graphics Code from Images, [Paper], [Project]

  • (arXiv 2023.12) Text-Conditioned Resampler For Long Form Video Understanding, [Paper]

  • (arXiv 2023.12) Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning, [Paper]

  • (arXiv 2023.12) A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise, [Paper], [Project]

  • (arXiv 2023.12) Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model, [Paper], [Project]

  • (arXiv 2023.12) M^2ConceptBase: A Fine-grained Aligned Multi-modal Conceptual Knowledge Base, [Paper]

  • (arXiv 2023.12) Language-conditioned Learning for Robotic Manipulation: A Survey, [Paper]

  • (arXiv 2023.12) TUNING LAYERNORM IN ATTENTION: TOWARDS EFFICIENT MULTI-MODAL LLM FINETUNING, [Paper]

  • (arXiv 2023.12) GSVA: Generalized Segmentation via Multimodal Large Language Models, [Paper], [Project]

  • (arXiv 2023.12) SILKIE: PREFERENCE DISTILLATION FOR LARGE VISUAL LANGUAGE MODELS, [Paper], [Project]

  • (arXiv 2023.12) AN EVALUATION OF GPT-4V AND GEMINI IN ONLINE VQA, [Paper]

  • (arXiv 2023.12) CEIR: CONCEPT-BASED EXPLAINABLE IMAGE REPRESENTATION LEARNING, [Paper], [Project]

  • (arXiv 2023.12) Language-Assisted 3D Scene Understanding, [Paper], [Project]

  • (arXiv 2023.12) M3DBench: Let’s Instruct Large Models with Multi-modal 3D Prompts, [Paper], [Project]

  • (arXiv 2023.12) Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AI, [Paper], [Project]

  • (arXiv 2023.12) FROM TEXT TO MOTION: GROUNDING GPT-4 IN A HUMANOID ROBOT “ALTER3”, [Paper], [Project]

  • (arXiv 2023.12) Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks, [Paper]

  • (arXiv 2023.12) LifelongMemory: Leveraging LLMs for Answering Queries in Egocentric Videos, [Paper]

  • (arXiv 2023.12) LEGO: Learning EGOcentric Action Frame Generation via Visual Instruction Tuning, [Paper]

  • (arXiv 2023.12) Localized Symbolic Knowledge Distillation for Visual Commonsense Models, [Paper], [Project]

  • (arXiv 2023.12) MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie Understanding, [Paper], [Project]

  • (arXiv 2023.12) Human Demonstrations are Generalizable Knowledge for Robots, [Paper]

  • (arXiv 2023.12) WonderJourney: Going from Anywhere to Everywhere, [Paper], [Project]

  • (arXiv 2023.12) VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models, [Paper], [Code]

  • (arXiv 2023.12) Text as Image: Learning Transferable Adapter for Multi-Label Classification, [Paper]

  • (arXiv 2023.12) Prompt Highlighter: Interactive Control for Multi-Modal LLMs, [Paper], [Project]

  • (arXiv 2023.12) Digital Life Project: Autonomous 3D Characters with Social Intelligence, [Paper], [Project]

  • (arXiv 2023.12) Generating Illustrated Instructions, [Paper], [Project]

  • (arXiv 2023.12) Aligning and Prompting Everything All at Once for Universal Visual Perception, [Paper], [Code]

  • (arXiv 2023.12) LEAP: LLM-Generation of Egocentric Action Programs, [Paper]

  • (arXiv 2023.12) OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition, [Paper], [Project]

  • (arXiv 2023.12) Merlin: Empowering Multimodal LLMs with Foresight Minds, [Paper], [Project]

  • (arXiv 2023.12) VIoTGPT: Learning to Schedule Vision Tools towards Intelligent Video Internet of Things, [Paper], [Code]

  • (arXiv 2023.12) Making Large Multimodal Models Understand Arbitrary Visual Prompts, [Paper], [Project]

2023.11

  • (arXiv 2023.11) Video-LLaVA: Learning United Visual Representation by Alignment Before Projection, [Paper], [Code]

  • (arXiv 2023.11) Self-Chained Image-Language Model for Video Localization and Question Answering, [Paper], [Code]

  • (arXiv 2023.11) Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning, [Paper], [Project]

  • (arXiv 2023.11) LALM: Long-Term Action Anticipation with Language Models, [Paper]

  • (arXiv 2023.11) Contrastive Vision-Language Alignment Makes Efficient Instruction Learner, [Paper], [Code]

  • (arXiv 2023.11) ChatIllusion: Efficient-Aligning Interleaved Generation ability with Visual Instruction Model, [Paper], [Code]

  • (arXiv 2023.11) MV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition, [Paper]

  • (arXiv 2023.11) VTimeLLM: Empower LLM to Grasp Video Moments, [Paper], [Code]

  • (arXiv 2023.11) Simple Semantic-Aided Few-Shot Learning, [Paper]

  • (arXiv 2023.11) LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning, [Paper], [Project]

  • (arXiv 2023.11) Detailed Human-Centric Text Description-Driven Large Scene Synthesis, [Paper]

  • (arXiv 2023.11) X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning, [Paper], [Code]

  • (arXiv 2023.11) CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation, [Paper], [Project]

  • (arXiv 2023.11) AvatarGPT: All-in-One Framework for Motion Understanding, Planning, Generation and Beyond, [Paper], [Project]

  • (arXiv 2023.11) InstructSeq: Unifying Vision Tasks with Instruction-conditioned Multi-modal Sequence Generation, [Paper], [Code]

  • (arXiv 2023.11) MLLMs-Augmented Visual-Language Representation Learning, [Paper], [Code]

  • (arXiv 2023.11) PoseGPT: Chatting about 3D Human Pose, [Paper], [Project]

  • (arXiv 2023.11) LLM-State: Expandable State Representation for Long-horizon Task Planning in the Open World, [Paper]

  • (arXiv 2023.11) UniIR: Training and Benchmarking Universal Multimodal Information Retrievers, [Paper], [Project]

  • (arXiv 2023.11) VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models, [Paper], [Code]

  • (arXiv 2023.11) MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning, [Paper], [Project]

  • (arXiv 2023.11) Knowledge Pursuit Prompting for Zero-Shot Multimodal Synthesis, [Paper]

  • (arXiv 2023.11) Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects, [Paper]

  • (arXiv 2023.11) OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation, [Paper], [Code]

  • (arXiv 2023.11) ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model, [Paper], [Project]

  • (arXiv 2023.11) VIM: Probing Multimodal Large Language Models for Visual Embedded Instruction Following, [Paper], [Project]

  • (arXiv 2023.11) Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models, [Paper], [Code]

  • (arXiv 2023.11) Self-correcting LLM-controlled Diffusion Models, [Paper]

  • (arXiv 2023.11) InterControl: Generate Human Motion Interactions by Controlling Every Joint, [Paper], [Code]

  • (arXiv 2023.11) DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback, [Paper], [Code]

  • (arXiv 2023.11) GAIA: A Benchmark for General AI Assistants, [Paper], [Project]

  • (arXiv 2023.11) PG-Video-LLaVA: Pixel Grounding Large Video-Language Models, [Paper], [Code]

  • (arXiv 2023.11) Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge, [Paper]

  • (arXiv 2023.11) AN EMBODIED GENERALIST AGENT IN 3D WORLD, [Paper], [Project]

  • (arXiv 2023.11) ShareGPT4V: Improving Large Multi-Modal Models with Better Captions, [Paper], [Project]

  • (arXiv 2023.11) KNVQA: A Benchmark for evaluation knowledge-based VQA, [Paper]

  • (arXiv 2023.11) GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning, [Paper], [Project]

  • (arXiv 2023.11) Boosting Audio-visual Zero-shot Learning with Large Language Models, [Paper], [Code]

  • (arXiv 2023.11) Few-Shot Classification & Segmentation Using Large Language Models Agent, [Paper]

  • (arXiv 2023.11) Igniting Language Intelligence: The Hitchhiker’s Guide From Chain-of-Thought Reasoning to Language Agents, [Paper], [Code]

  • (arXiv 2023.11) VLM-Eval: A General Evaluation on Video Large Language Models, [Paper]

  • (arXiv 2023.11) LLMs as Visual Explainers: Advancing Image Classification with Evolving Visual Descriptions, [Paper], [Code]

  • (arXiv 2023.11) LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge, [Paper], [Project]

  • (arXiv 2023.11) Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding, [Paper], [Code]

  • (arXiv 2023.11) How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model, [Paper]

  • (arXiv 2023.11) Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models, [Paper], [Code]

  • (arXiv 2023.11) Towards Open-Ended Visual Recognition with Large Language Model, [Paper], [Code]

  • (arXiv 2023.11) Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models, [Paper], [Code]

  • (arXiv 2023.11) VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS, [Paper], [Project]

  • (arXiv 2023.11) VOLCANO: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision, [Paper], [Code]

  • (arXiv 2023.11) AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation, [Paper], [Code]

  • (arXiv 2023.11) Analyzing Modular Approaches for Visual Question Decomposition, [Paper], [Code]

  • (arXiv 2023.11) LayoutPrompter: Awaken the Design Ability of Large Language Models, [Paper], [Code]

  • (arXiv 2023.11) PerceptionGPT: Effectively Fusing Visual Perception into LLM, [Paper]

  • (arXiv 2023.11) InfMLLM: A Unified Framework for Visual-Language Tasks, [Paper], [Code]

  • (arXiv 2023.11) WHAT LARGE LANGUAGE MODELS BRING TO TEXTRICH VQA?, [Paper]

  • (arXiv 2023.11) Story-to-Motion: Synthesizing Infinite and Controllable Character Animation from Long Text, [Paper], [Project]

  • (arXiv 2023.11) GPT-4V(ision) as A Social Media Analysis Engine, [Paper], [Code]

  • (arXiv 2023.11) GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation, [Paper], [Code]

  • (arXiv 2023.11) To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning, [Paper], [Code]

  • (arXiv 2023.11) SPHINX: THE JOINT MIXING OF WEIGHTS, TASKS, AND VISUAL EMBEDDINGS FOR MULTI-MODAL LARGE LANGUAGE MODELS, [Paper], [Code]

  • (arXiv 2023.11) ADAPT: As-Needed Decomposition and Planning with Language Models, [Paper], [Project]

  • (arXiv 2023.11) JARVIS-1: Open-world Multi-task Agents with Memory-Augmented Multimodal Language Models, [Paper], [Project]

  • (arXiv 2023.11) Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks, [Paper]

  • (arXiv 2023.11) Multitask Multimodal Prompted Training for Interactive Embodied Task Completion, [Paper], [Code]

  • (arXiv 2023.11) TEAL: TOKENIZE AND EMBED ALL FOR MULTIMODAL LARGE LANGUAGE MODELS, [Paper]

  • (arXiv 2023.11) u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model, [Paper]

  • (arXiv 2023.11) LLAVA-PLUS: LEARNING TO USE TOOLS FOR CREATING MULTIMODAL AGENTS, [Paper], [Project]

  • (arXiv 2023.11) Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models, [Paper], [Code]

  • (arXiv 2023.11) OtterHD: A High-Resolution Multi-modality Model, [Paper], [Code]

  • (arXiv 2023.11) NExT-Chat: An LMM for Chat, Detection and Segmentation, [Paper], [Project]

  • (arXiv 2023.11) GENOME: GENERATIVE NEURO-SYMBOLIC VISUAL REASONING BY GROWING AND REUSING MODULES, [Paper], [Project]

  • (arXiv 2023.11) MAKE A DONUT: LANGUAGE-GUIDED HIERARCHICAL EMD-SPACE PLANNING FOR ZERO-SHOT DEFORMABLE OBJECT MANIPULATION, [Paper]

  • (arXiv 2023.11) Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs, [Paper], [Code]

  • (arXiv 2023.11) Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models, [Paper]

  • (arXiv 2023.11) ROBOGEN: TOWARDS UNLEASHING INFINITE DATA FOR AUTOMATED ROBOT LEARNING VIA GENERATIVE SIMULATION, [Paper]

2023.10

  • (arXiv 2023.10) MINIGPT-5: INTERLEAVED VISION-AND-LANGUAGE GENERATION VIA GENERATIVE VOKENS, [Paper], [Code]

  • (arXiv 2023.10) What’s “up” with vision-language models? Investigating their struggle with spatial reasoning, [Paper], [Code]

  • (arXiv 2023.10) APOLLO: ZERO-SHOT MULTIMODAL REASONING WITH MULTIPLE EXPERTS, [Paper], [Code]

  • (arXiv 2023.10) ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common Sense, [Paper]

  • (arXiv 2023.10) Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models, [Paper], [Project]

  • (arXiv 2023.10) LARGE LANGUAGE MODELS AS GENERALIZABLE POLICIES FOR EMBODIED TASKS, [Paper], [Project]

  • (arXiv 2023.10) Humanoid Agents: Platform for Simulating Human-like Generative Agents, [Paper], [Project]

  • (arXiv 2023.10) REVO-LION: EVALUATING AND REFINING VISION-LANGUAGE INSTRUCTION TUNING DATASETS, [Paper], [Code]

  • (arXiv 2023.10) How (not) to ensemble LVLMs for VQA, [Paper]

  • (arXiv 2023.10) What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models, [Paper], [Code]

  • (arXiv 2023.10) Words into Action: Learning Diverse Humanoid Robot Behaviors using Language Guided Iterative Motion Refinement, [Paper], [Code]

  • (arXiv 2023.10) GameGPT: Multi-agent Collaborative Framework for Game Development, [Paper]

  • (arXiv 2023.10) STEVE-EYE: EQUIPPING LLM-BASED EMBODIED AGENTS WITH VISUAL PERCEPTION IN OPEN WORLDS, [Paper]

  • (arXiv 2023.10) BENCHMARKING SEQUENTIAL VISUAL INPUT REASONING AND PREDICTION IN MULTIMODAL LARGE LANGUAGE MODELS, [Paper], [Code]

  • (arXiv 2023.10) A Simple Baseline for Knowledge-Based Visual Question Answering, [Paper], [Code]

  • (arXiv 2023.10) Interactive Robot Learning from Verbal Correction, [Paper], [Project]

  • (arXiv 2023.10) Exploring Question Decomposition for Zero-Shot VQA, [Paper], [Project]

  • (arXiv 2023.10) RIO: A Benchmark for Reasoning Intention-Oriented Objects in Open Environments, [Paper], [Project]

  • (arXiv 2023.10) An Early Evaluation of GPT-4V(ision), [Paper], [Code]

  • (arXiv 2023.10) DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models, [Paper], [Project]

  • (arXiv 2023.10) CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images, [Paper], [Code]

  • (arXiv 2023.10) VIDEOPROMPTER: AN ENSEMBLE OF FOUNDATIONAL MODELS FOR ZERO-SHOT VIDEO UNDERSTANDING, [Paper]

  • (arXiv 2023.10) Inject Semantic Concepts into Image Tagging for Open-Set Recognition, [Paper], [Code]

  • (arXiv 2023.10) Woodpecker: Hallucination Correction for Multimodal Large Language Models, [Paper], [Code]

  • (arXiv 2023.10) Visual Cropping Improves Zero-Shot Question Answering of Multimodal Large Language Models, [Paper], [Code]

  • (arXiv 2023.10) Large Language Models are Temporal and Causal Reasoners for Video Question Answering, [Paper], [Code]

  • (arXiv 2023.10) What’s Left? Concept Grounding with Logic-Enhanced Foundation Models, [Paper]

  • (arXiv 2023.10) Evaluating Spatial Understanding of Large Language Models, [Paper]

  • (arXiv 2023.10) Learning Reward for Physical Skills using Large Language Model, [Paper]

  • (arXiv 2023.10) CREATIVE ROBOT TOOL USE WITH LARGE LANGUAGE MODELS, [Paper], [Project]

  • (arXiv 2023.10) Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models, [Paper], [Project]

  • (arXiv 2023.10) Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning, [Paper], [Project]

  • (arXiv 2023.10) LARGE LANGUAGE MODELS CAN Share IMAGES, TOO! [Paper], [Code]

  • (arXiv 2023.10) Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond, [Paper]

  • (arXiv 2023.10) HALLUSIONBENCH: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(vision), LLaVA-1.5, and Other Multi-modality Models, [Paper], [Code]

  • (arXiv 2023.10) Can Language Models Laugh at YouTube Short-form Videos? [Paper], [Code]

  • (arXiv 2023.10) Large Language Models are Visual Reasoning Coordinators, [Paper], [Code]

  • (arXiv 2023.10) Language Models as Zero-Shot Trajectory Generators, [Paper], [Project]

  • (arXiv 2023.10) Localizing Active Objects from Egocentric Vision with Symbolic World Knowledge, [Paper], [Code]

  • (arXiv 2023.10) Multimodal Large Language Model for Visual Navigation, [Paper]

  • (arXiv 2023.10) MAKING MULTIMODAL GENERATION EASIER: WHEN DIFFUSION MODELS MEET LLMS, [Paper], [Code]

  • (arXiv 2023.10) Open X-Embodiment: Robotic Learning Datasets and RT-X Models, [Paper], [Project]

  • (arXiv 2023.10) Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT, [Paper], [Code]

  • (arXiv 2023.10) Lost in Translation: When GPT-4V(ision) Can’t See Eye to Eye with Text A Vision-Language-Consistency Analysis of VLLMs and Beyond, [Paper]

  • (arXiv 2023.10) Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models, [Paper]

  • (arXiv 2023.10) VLIS: Unimodal Language Models Guide Multimodal Language Generation, [Paper], [Code]

  • (arXiv 2023.10) CLIN: A CONTINUALLY LEARNING LANGUAGE AGENT FOR RAPID TASK ADAPTATION AND GENERALIZATION, [Paper], [Project]

  • (arXiv 2023.10) Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning, [Paper]

  • (arXiv 2023.10) Lost in Translation: When GPT-4V(ision) Can’t See Eye to Eye with Text A Vision-Language-Consistency Analysis of VLLMs and Beyond, [Paper]

  • (arXiv 2023.10) FROZEN TRANSFORMERS IN LANGUAGE MODELS ARE EFFECTIVE VISUAL ENCODER LAYERS, [Paper], [Code]

  • (arXiv 2023.10) CLAIR: Evaluating Image Captions with Large Language Models, [Paper], [Project]

  • (arXiv 2023.10) 3D-GPT: PROCEDURAL 3D MODELING WITH LARGE LANGUAGE MODELS, [Paper], [Project]

  • (arXiv 2023.10) Automated Natural Language Explanation of Deep Visual Neurons with Large Models, [Paper]

  • (arXiv 2023.10) Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V, [Paper], [Project]

  • (arXiv 2023.10) EvalCrafter: Benchmarking and Evaluating Large Video Generation Models, [Paper], [Project]

  • (arXiv 2023.10) MISAR: A MULTIMODAL INSTRUCTIONAL SYSTEM WITH AUGMENTED REALITY, [Paper], [Code]

  • (arXiv 2023.10) NON-INTRUSIVE ADAPTATION: INPUT-CENTRIC PARAMETER-EFFICIENT FINE-TUNING FOR VERSATILE MULTIMODAL MODELING, [Paper]

  • (arXiv 2023.10) LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation, [Paper], [Project]

  • (arXiv 2023.10) ChatGPT-guided Semantics for Zero-shot Learning, [Paper]

  • (arXiv 2023.10) On the Benefit of Generative Foundation Models for Human Activity Recognition, [Paper]

  • (arXiv 2023.10) DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning, [Paper], [Project]

  • (arXiv 2023.10) Interactive Task Planning with Language Models, [Paper], [Project]

  • (arXiv 2023.10) Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance, [Paper], [Project]

  • (arXiv 2023.10) Penetrative AI: Making LLMs Comprehend the Physical World, [Paper]

  • (arXiv 2023.10) BONGARD-OPENWORLD: FEW-SHOT REASONING FOR FREE-FORM VISUAL CONCEPTS IN THE REAL WORLD, [Paper], [Project]

  • (arXiv 2023.10) ViPE: Visualise Pretty-much Everything, [Paper]

  • (arXiv 2023.10) MINIGPT-V2: LARGE LANGUAGE MODEL AS A UNIFIED INTERFACE FOR VISION-LANGUAGE MULTITASK LEARNING, [Paper], [Project]

  • (arXiv 2023.10) MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations, [Paper]

  • (arXiv 2023.10) LLM BLUEPRINT: ENABLING TEXT-TO-IMAGE GENERATION WITH COMPLEX AND DETAILED PROMPTS, [Paper]

  • (arXiv 2023.10) VIDEO LANGUAGE PLANNING, [Paper], [Project]

  • (arXiv 2023.10) Dobby: A Conversational Service Robot Driven by GPT-4, [Paper]

  • (arXiv 2023.10) CoPAL: Corrective Planning of Robot Actions with Large Language Models, [Paper]

  • (arXiv 2023.10) Forgetful Large Language Models: Lessons Learned from Using LLMs in Robot Programming, [Paper]

  • (arXiv 2023.10) TREE-PLANNER: EFFICIENT CLOSE-LOOP TASK PLANNING WITH LARGE LANGUAGE MODELS, [Paper], [Project]

  • (arXiv 2023.10) TOWARDS ROBUST MULTI-MODAL REASONING VIA MODEL SELECTION, [Paper], [Code]

  • (arXiv 2023.10) FERRET: REFER AND GROUND ANYTHING ANYWHERE AT ANY GRANULARITY, [Paper], [Code]

  • (arXiv 2023.10) FROM SCARCITY TO EFFICIENCY: IMPROVING CLIP TRAINING VIA VISUAL-ENRICHED CAPTIONS, [Paper]

  • (arXiv 2023.10) OPENLEAF: OPEN-DOMAIN INTERLEAVED IMAGE-TEXT GENERATION AND EVALUATION, [Paper]

  • (arXiv 2023.10) Can We Edit Multimodal Large Language Models? [Paper], [Code]

  • (arXiv 2023.10) VISUAL DATA-TYPE UNDERSTANDING DOES NOT EMERGE FROM SCALING VISION-LANGUAGE MODELS, [Paper], [Code]

  • (arXiv 2023.10) Idea2Img: Iterative Self-Refinement with GPT-4V(vision) for Automatic Image Design and Generation, [Paper], [Project]

  • (arXiv 2023.10) OCTOPUS: EMBODIED VISION-LANGUAGE PROGRAMMER FROM ENVIRONMENTAL FEEDBACK, [Paper], [Project]

2023.9

  • (arXiv 2023.9) LMEye: An Interactive Perception Network for Large Language Models, [Paper], [Code]

  • (arXiv 2023.9) DynaCon: Dynamic Robot Planner with Contextual Awareness via LLMs, [Paper], [Project]

  • (arXiv 2023.9) AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model, [Paper]

  • (arXiv 2023.9) ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning, [Paper], [Project]

  • (arXiv 2023.9) LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement, [Paper], [Code]

  • (arXiv 2023.9) ONE FOR ALL: VIDEO CONVERSATION IS FEASIBLE WITHOUT VIDEO INSTRUCTION TUNING, [Paper]

  • (arXiv 2023.9) Verifiable Learned Behaviors via Motion Primitive Composition: Applications to Scooping of Granular Media, [Paper]

  • (arXiv 2023.9) Human-Assisted Continual Robot Learning with Foundation Models, [Paper], [Project]

  • (arXiv 2023.9) InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition, [Paper], [Code]

  • (arXiv 2023.9) VIDEODIRECTORGPT: CONSISTENT MULTI-SCENE VIDEO GENERATION VIA LLM-GUIDED PLANNING, [Paper], [Project]

  • (arXiv 2023.9) Text-to-Image Generation for Abstract Concepts, [Paper]

  • (arXiv 2023.9) Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator, [Paper], [Code]

  • (arXiv 2023.9) ALIGNING LARGE MULTIMODAL MODELS WITH FACTUALLY AUGMENTED RLHF, [Paper], [Project]

  • (arXiv 2023.9) Self-Recovery Prompting: Promptable General Purpose Service Robot System with Foundation Models and Self-Recovery, [Paper], [Project]

  • (arXiv 2023.9) Q-BENCH: A BENCHMARK FOR GENERAL-PURPOSE FOUNDATION MODELS ON LOW-LEVEL VISION, [Paper]

  • (arXiv 2023.9) DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention, [Paper], [Code]

  • (arXiv 2023.9) LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition, [Paper], [Code]

  • (arXiv 2023.9) LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent, [Paper], [Project]

  • (arXiv 2023.9) Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models, [Paper], [Code]

  • (arXiv 2023.9) STRUCTCHART: PERCEPTION, STRUCTURING, REASONING FOR VISUAL CHART UNDERSTANDING, [Paper]

  • (arXiv 2023.9) DREAMLLM: SYNERGISTIC MULTIMODAL COMPREHENSION AND CREATION, [Paper], [Project]

  • (arXiv 2023.9) A LARGE-SCALE DATASET FOR AUDIO-LANGUAGE REPRESENTATION LEARNING, [Paper], [Project]

  • (arXiv 2023.9) YOU ONLY LOOK AT SCREENS: MULTIMODAL CHAIN-OF-ACTION AGENTS, [Paper], [Code]

  • (arXiv 2023.9) SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models, [Paper], [Project]

  • (arXiv 2023.9) Conformal Temporal Logic Planning using Large Language Models: Knowing When to Do What and When to Ask for Help, [Paper], [Project]

  • (arXiv 2023.9) Investigating the Catastrophic Forgetting in Multimodal Large Language Models, [Paper]

  • (arXiv 2023.9) Specification-Driven Video Search via Foundation Models and Formal Verification, [Paper]

  • (arXiv 2023.9) Language as the Medium: Multimodal Video Classification through text only, [Paper]

  • (arXiv 2023.9) Multimodal Foundation Models: From Specialists to General-Purpose Assistants, [Paper]

  • (arXiv 2023.9) TEXTBIND: Multi-turn Interleaved Multimodal Instruction-following, [Paper], [Project]

  • (arXiv 2023.9) Prompt a Robot to Walk with Large Language Models, [Paper], [Project]

  • (arXiv 2023.9) Grasp-Anything: Large-scale Grasp Dataset from Foundation Models, [Paper], [Project]

  • (arXiv 2023.9) MMICL: EMPOWERING VISION-LANGUAGE MODEL WITH MULTI-MODAL IN-CONTEXT LEARNING, [Paper], [Code]

  • (arXiv 2023.9) SwitchGPT: Adapting Large Language Models for Non-Text Outputs, [Paper], [Code]

  • (arXiv 2023.9) UNIFIED HUMAN-SCENE INTERACTION VIA PROMPTED CHAIN-OF-CONTACTS, [Paper], [Code]

  • (arXiv 2023.9) Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models, [Paper]

  • (arXiv 2023.9) NExT-GPT: Any-to-Any Multimodal LLM, [Paper], [Project]

  • (arXiv 2023.9) Multi3DRefer: Grounding Text Description to Multiple 3D Objects, [Paper], [Project]

  • (arXiv 2023.9) Language Models as Black-Box Optimizers for Vision-Language Models, [Paper]

  • (arXiv 2023.9) Evaluation and Mitigation of Agnosia in Multimodal Large Language Models, [Paper]

  • (arXiv 2023.9) Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models, [Paper], [Code]

  • (arXiv 2023.9) Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment, [Paper]

  • (arXiv 2023.9) ImageBind-LLM: Multi-modality Instruction Tuning, [Paper], [Code]

  • (arXiv 2023.9) Developmental Scaffolding with Large Language Models, [Paper]

  • (arXiv 2023.9) Gesture-Informed Robot Assistance via Foundation Models, [Paper], [Project]

  • (arXiv 2023.9) Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging, [Paper]

  • (arXiv 2023.9) Large AI Model Empowered Multimodal Semantic Communications, [Paper]

  • (arXiv 2023.9) CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection, [Paper], [Project]

  • (arXiv 2023.9) Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning, [Paper]

  • (arXiv 2023.9) CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning, [Paper]

  • (arXiv 2023.9) Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following, [Paper], [Code]

2023.8

  • (arXiv 2023.8) Planting a SEED of Vision in Large Language Model, [Paper], [Code]

  • (arXiv 2023.8) EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE, [Paper]

  • (arXiv 2023.8) Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images, [Paper], [Project]

  • (arXiv 2023.8) Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis, [Paper]

  • (arXiv 2023.8) Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models, [Paper], [Code]

  • (arXiv 2023.8) PointLLM: Empowering Large Language Models to Understand Point Clouds, [Paper], [Project]

  • (arXiv 2023.8) TouchStone: Evaluating Vision-Language Models by Language Models, [Paper], [Code]

  • (arXiv 2023.8) Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes, [Paper], [Project]

  • (arXiv 2023.8) WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model, [Paper]

  • (arXiv 2023.8) ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning, [Paper], [Code]

  • (arXiv 2023.8) LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks, [Paper]

  • (arXiv 2023.8) Evaluation and Analysis of Hallucination in Large Vision-Language Models, [Paper]

  • (arXiv 2023.8) MLLM-DataEngine: An Iterative Refinement Approach for MLLM, [Paper]

  • (arXiv 2023.8) Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models, [Paper]

  • (arXiv 2023.8) Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining? [Paper], [Code]

  • (arXiv 2023.8) VIGC: Visual Instruction Generation and Correction, [Paper]

  • (arXiv 2023.8) Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment, [Paper]

  • (arXiv 2023.8) Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities, [Paper], [Code]

  • (arXiv 2023.8) DIFFUSION LANGUAGE MODELS CAN PERFORM MANY TASKS WITH SCALING AND INSTRUCTION-FINETUNING, [Paper], [Code]

  • (arXiv 2023.8) CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images, [Paper], [Project]

  • (arXiv 2023.8) ProAgent: Building Proactive Cooperative AI with Large Language Models, [Paper], [Project]

  • (arXiv 2023.8) ROSGPT_Vision: Commanding Robots Using Only Language Models’ Prompts, [Paper], [Code]

  • (arXiv 2023.8) StoryBench: A Multifaceted Benchmark for Continuous Story Visualization, [Paper], [Code]

  • (arXiv 2023.8) Tackling Vision Language Tasks Through Learning Inner Monologues, [Paper]

  • (arXiv 2023.8) ExpeL: LLM Agents Are Experiential Learners, [Paper]

  • (arXiv 2023.8) On the Adversarial Robustness of Multi-Modal Foundation Models, [Paper]

  • (arXiv 2023.8) WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models, [Paper], [Project]

  • (arXiv 2023.8) March in Chat: Interactive Prompting for Remote Embodied Referring Expression, [Paper], [Code]

  • (arXiv 2023.8) BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions, [Paper], [Code]

  • (arXiv 2023.8) VIT-LENS: Towards Omni-modal Representations, [Paper], [Code]

  • (arXiv 2023.8) StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data, [Paper], [Project]

  • (arXiv 2023.8) PUMGPT: A Large Vision-Language Model for Product Understanding, [Paper]

  • (arXiv 2023.8) Link-Context Learning for Multimodal LLMs, [Paper], [Code]

  • (arXiv 2023.8) Detecting and Preventing Hallucinations in Large Vision Language Models, [Paper]

  • (arXiv 2023.8) VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use, [Paper], [Project]

  • (arXiv 2023.8) Foundation Model based Open Vocabulary Task Planning and Executive System for General Purpose Service Robots, [Paper]

  • (arXiv 2023.8) LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation, [Paper], [Project]

  • (arXiv 2023.8) OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation, [Paper]

  • (arXiv 2023.8) EMPOWERING VISION-LANGUAGE MODELS TO FOLLOW INTERLEAVED VISION-LANGUAGE INSTRUCTIONS, [Paper], [Code]

  • (arXiv 2023.8) 3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment, [Paper], [Project]

  • (arXiv 2023.8) Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs, [Paper], [Project]

  • (arXiv 2023.8) AgentBench: Evaluating LLMs as Agents, [Paper], [Project]

  • (arXiv 2023.8) Learning Concise and Descriptive Attributes for Visual Recognition, [Paper]

  • (arXiv 2023.8) Tiny LVLM-eHub: Early Multimodal Experiments with Bard, [Paper], [Project]

  • (arXiv 2023.8) MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities, [Paper], [Code]

  • (arXiv 2023.8) RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension, [Paper], [Code]

  • (arXiv 2023.8) Learning to Model the World with Language, [Paper], [Project]

  • (arXiv 2023.8) The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World, [Paper], [Code]

  • (arXiv 2023.8) Multimodal Neurons in Pretrained Text-Only Transformers, [Paper], [Project]

  • (arXiv 2023.8) LISA: REASONING SEGMENTATION VIA LARGE LANGUAGE MODEL, [Paper], [Code]

2023.7

  • (arXiv 2023.7) Caption Anything: Interactive Image Description with Diverse Multimodal Controls, [Paper], [Code]

  • (arXiv 2023.7) DesCo: Learning Object Recognition with Rich Language Descriptions, [Paper]

  • (arXiv 2023.7) KOSMOS-2: Grounding Multimodal Large Language Models to the World, [Paper], [Project]

  • (arXiv 2023.7) MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models, [Paper], [Code]

  • (arXiv 2023.7) Evaluating ChatGPT and GPT-4 for Visual Programming, [Paper]

  • (arXiv 2023.7) SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension, [Paper], [Code]

  • (arXiv 2023.7) AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos? [Paper], [Project]

  • (arXiv 2023.7) Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Tasks, [Paper]

  • (arXiv 2023.7) MovieChat: From Dense Token to Sparse Memory for Long Video Understanding, [Paper], [Project]

  • (arXiv 2023.7) Large Language Models as General Pattern Machines, [Paper], [Project]

  • (arXiv 2023.7) How Good is Google Bard’s Visual Understanding? An Empirical Study on Open Challenges, [Paper], [Project]

  • (arXiv 2023.7) RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control, [Paper], [Project]

  • (arXiv 2023.7) Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition, [Paper], [Project]

  • (arXiv 2023.7) GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping, [Paper], [Project]

  • (arXiv 2023.7) CARTIER: Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots, [Paper]

  • (arXiv 2023.7) 3D-LLM: Injecting the 3D World into Large Language Models, [Paper], [Project]

  • (arXiv 2023.7) Generative Pretraining in Multimodality, [Paper], [Code]

  • (arXiv 2023.7) VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models, [Paper], [Project]

  • (arXiv 2023.7) VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View, [Paper]

  • (arXiv 2023.7) SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Task Planning, [Paper], [Project]

  • (arXiv 2023.7) Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts, [Paper]

  • (arXiv 2023.7) InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation, [Paper], [Data]

  • (arXiv 2023.7) MBLIP: EFFICIENT BOOTSTRAPPING OF MULTILINGUAL VISION-LLMS, [Paper], [Code]

  • (arXiv 2023.7) Bootstrapping Vision-Language Learning with Decoupled Language Pre-training, [Paper]

  • (arXiv 2023.7) BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs, [Paper], [Project]

  • (arXiv 2023.7) ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning, [Paper], [Project]

  • (arXiv 2023.7) TOWARDS A UNIFIED AGENT WITH FOUNDATION MODELS, [Paper]

  • (arXiv 2023.7) Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners, [Paper], [Project]

  • (arXiv 2023.7) Building Cooperative Embodied Agents Modularly with Large Language Models, [Paper], [Project]

  • (arXiv 2023.7) Embodied Task Planning with Large Language Models, [Paper], [Project]

  • (arXiv 2023.7) What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?, [Paper], [Project]

  • (arXiv 2023.7) GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest, [Paper], [Code]

  • (arXiv 2023.7) JourneyDB: A Benchmark for Generative Image Understanding, [Paper], [Code]

  • (arXiv 2023.7) DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment, [Paper], [Project]

  • (arXiv 2023.7) Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset, [Paper], [Code]

  • (arXiv 2023.7) Visual Instruction Tuning with Polite Flamingo, [Paper], [Code]

  • (arXiv 2023.7) Statler: State-Maintaining Language Models for Embodied Reasoning, [Paper], [Project]

  • (arXiv 2023.7) SCITUNE: Aligning Large Language Models with Scientific Multimodal Instructions, [Paper]

  • (arXiv 2023.7) SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs, [Paper], [Code]

  • (arXiv 2023.7) KITE: Keypoint-Conditioned Policies for Semantic Manipulation, [Paper], [Project]

2023.6

  • (arXiv 2023.6) MultiModal-GPT: A Vision and Language Model for Dialogue with Humans, [Paper], [Code]

  • (arXiv 2023.6) InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language, [Paper], [Code]

  • (arXiv 2023.6) InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning, [Paper], [Code]

  • (arXiv 2023.6) LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark, [Paper], [Code]

  • (arXiv 2023.6) Scalable 3D Captioning with Pretrained Models, [Paper], [Code]

  • (arXiv 2023.6) AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers, [Paper], [Code]

  • (arXiv 2023.6) VALLEY: VIDEO ASSISTANT WITH LARGE LANGUAGE MODEL ENHANCED ABILITY, [Paper], [Code]

  • (arXiv 2023.6) Pave the Way to Grasp Anything: Transferring Foundation Models for Universal Pick-Place Robots, [Paper]

  • (arXiv 2023.6) LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models, [Paper]

  • (arXiv 2023.6) AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn, [Paper], [Project]

  • (arXiv 2023.6) Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models, [Paper]

  • (arXiv 2023.6) MACAW-LLM: MULTI-MODAL LANGUAGE MODELING WITH IMAGE, AUDIO, VIDEO, AND TEXT INTEGRATION, [Paper], [Code]

  • (arXiv 2023.6) Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering, [Paper]

  • (arXiv 2023.6) Language to Rewards for Robotic Skill Synthesis, [Paper], [Project]

  • (arXiv 2023.6) Toward Grounded Social Reasoning, [Paper], [Code]

  • (arXiv 2023.6) Improving Image Captioning Descriptiveness by Ranking and LLM-based Fusion, [Paper], [Code]

  • (arXiv 2023.6) RM-PRT: Realistic Robotic Manipulation Simulator and Benchmark with Progressive Reasoning Tasks, [Paper], [Code]

  • (arXiv 2023.6) Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning, [Paper], [Project]

  • (arXiv 2023.6) Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language, [Paper], [Code]

  • (arXiv 2023.6) LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding, [Paper], [Project]

  • (arXiv 2023.6) OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding, [Paper], [Project]

  • (arXiv 2023.6) Statler: State-Maintaining Language Models for Embodied Reasoning, [Paper], [Project]

  • (arXiv 2023.6) CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents, [Paper]

  • (arXiv 2023.6) Mass-Producing Failures of Multimodal Systems with Language Models, [Paper], [Code]

  • (arXiv 2023.6) SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer, [Paper]

  • (arXiv 2023.6) SPRINT: SCALABLE POLICY PRE-TRAINING VIA LANGUAGE INSTRUCTION RELABELING, [Paper], [Project]

  • (arXiv 2023.6) MotionGPT: Finetuned LLMs are General-Purpose Motion Generators, [Paper], [Project]

  • (arXiv 2023.6) MIMIC-IT: Multi-Modal In-Context Instruction Tuning, [Paper], [Code]

  • (arXiv 2023.6) Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models, [Paper]

2023.5

  • (arXiv 2023.5) IMAGENETVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories, [Paper], [Code]

  • (arXiv 2023.5) ECHO: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric ReasOning, [Paper], [Code]

  • (arXiv 2023.5) PROMPTING LANGUAGE-INFORMED DISTRIBUTION FOR COMPOSITIONAL ZERO-SHOT LEARNING, [Paper]

  • (arXiv 2023.5) Exploring Diverse In-Context Configurations for Image Captioning, [Paper]

  • (arXiv 2023.5) Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models, [Paper], [Code]

  • (arXiv 2023.5) IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models, [Paper], [Code]

  • (arXiv 2023.5) LayoutGPT: Compositional Visual Planning and Generation with Large Language Models, [Paper], [Code]

  • (arXiv 2023.5) Enhance Reasoning Ability of Visual-Language Models via Large Language Models, [Paper]

  • (arXiv 2023.5) DetGPT: Detect What You Need via Reasoning, [Paper], [Code]

  • (arXiv 2023.5) Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model, [Paper], [Code]

  • (arXiv 2023.5) TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding, [Paper]

  • (arXiv 2023.5) i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data, [Paper]

  • (arXiv 2023.5) What Makes for Good Visual Tokenizers for Large Language Models?, [Paper], [Code]

  • (arXiv 2023.5) Interactive Data Synthesis for Systematic Vision Adaptation via LLMs-AIGCs Collaboration, [Paper], [Code]

  • (arXiv 2023.5) X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages, [Paper], [Project]

  • (arXiv 2023.5) Otter: A Multi-Modal Model with In-Context Instruction Tuning, [Paper], [Code]

  • (arXiv 2023.5) VideoChat: Chat-Centric Video Understanding, [Paper], [Code]

  • (arXiv 2023.5) Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering, [Paper], [Code]

  • (arXiv 2023.5) VIMA: General Robot Manipulation with Multimodal Prompts, [Paper], [Project]

  • (arXiv 2023.5) TidyBot: Personalized Robot Assistance with Large Language Models, [Paper], [Project]

  • (arXiv 2023.5) Training Diffusion Models with Reinforcement Learning, [Paper], [Project]

  • (arXiv 2023.5) EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought, [Paper], [Project]

  • (arXiv 2023.5) ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4, [Paper], [Code]

  • (arXiv 2023.5) Evaluating Object Hallucination in Large Vision-Language Models, [Paper], [Code]

  • (arXiv 2023.5) LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation, [Paper], [Code]

  • (arXiv 2023.5) VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks, [Paper], [Code]

  • (arXiv 2023.5) OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding, [Paper], [Project]

  • (arXiv 2023.5) Towards A Foundation Model for Generalist Robots: Diverse Skill Learning at Scale via Automated Task and Scene Generation, [Paper]

  • (arXiv 2023.5) An Android Robot Head as Embodied Conversational Agent, [Paper]

  • (arXiv 2023.5) Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model, [Paper], [Code]

  • (arXiv 2023.5) Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision, [Paper], [Project]

  • (arXiv 2023.5) Multimodal Procedural Planning via Dual Text-Image Prompting, [Paper], [Code]

  • (arXiv 2023.5) ArK: Augmented Reality with Knowledge Interactive Emergent Ability, [Paper]

2023.4

  • (arXiv 2023.4) LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model, [Paper], [Code]

  • (arXiv 2023.4) Multimodal Grounding for Embodied AI via Augmented Reality Headsets for Natural Language Driven Task Planning, [Paper]

  • (arXiv 2023.4) mPLUG-Owl : Modularization Empowers Large Language Models with Multimodality, [Paper], [Code]

  • (arXiv 2023.4) ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System, [Paper], [Project]

  • (arXiv 2023.4) ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT, [Paper]

  • (arXiv 2023.4) Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT, [Paper]

  • (arXiv 2023.4) Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT, [Paper], [Code]

  • (arXiv 2023.4) Can GPT-4 Perform Neural Architecture Search?, [Paper], [Code]

  • (arXiv 2023.4) MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models, [Paper], [Project]

  • (arXiv 2023.4) SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation, [Paper], [Project]

  • (arXiv 2023.4) LLM as A Robotic Brain: Unifying Egocentric Memory and Control, [Paper]

  • (arXiv 2023.4) Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models, [Paper], [Project]

  • (arXiv 2023.4) Visual Instruction Tuning, [Paper], [Project]

  • (arXiv 2023.4) MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models, [Paper], [Project]

  • (arXiv 2023.4) RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment, [Paper], [Code]

  • (arXiv 2023.4) Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved With Text, [Paper], [Code]

  • (arXiv 2023.4) ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance, [Paper], [Code]

  • (arXiv 2023.4) HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face, [Paper], [Code]

  • (arXiv 2023.4) ERRA: An Embodied Representation and Reasoning Architecture for Long-horizon Language-conditioned Manipulation Tasks, [Paper], [Code]

  • (arXiv 2023.4) Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT, [Paper]

  • (arXiv 2023.4) ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application, [Paper], [Code]

  • (arXiv 2023.4) OpenAGI: When LLM Meets Domain Experts, [Paper], [Code]

  • (arXiv 2023.4) Video ChatCaptioner: Towards the Enriched Spatiotemporal Descriptions, [Paper], [Code]

2023.3

  • (arXiv 2023.3) Open-World Object Manipulation using Pre-Trained Vision-Language Models, [Paper], [Project]

  • (arXiv 2023.3) Grounded Decoding: Guiding Text Generation with Grounded Models for Robot Control, [Paper], [Project]

  • (arXiv 2023.3) Task and Motion Planning with Large Language Models for Object Rearrangement, [Paper], [Project]

  • (arXiv 2023.3) RE-MOVE: An Adaptive Policy Design Approach for Dynamic Environments via Language-Based Feedback, [Paper], [Project]

  • (arXiv 2023.3) Chat with the Environment: Interactive Multimodal Perception using Large Language Models, [Paper]

  • (arXiv 2023.3) MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge, [Paper], [Code]

  • (arXiv 2023.3) DialogPaint: A Dialog-based Image Editing Model, [Paper]

  • (arXiv 2023.3) MM-REACT : Prompting ChatGPT for Multimodal Reasoning and Action, [Paper], [Project]

  • (arXiv 2023.3) eP-ALM: Efficient Perceptual Augmentation of Language Models, [Paper], [Code]

  • (arXiv 2023.3) Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting, [Paper], [Project]

  • (arXiv 2023.3) LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention, [Paper], [Code]

  • (arXiv 2023.3) MULTIMODAL ANALOGICAL REASONING OVER KNOWLEDGE GRAPHS, [Paper], [Code]

  • (arXiv 2023.3) CAN LARGE LANGUAGE MODELS DESIGN A ROBOT? [Paper]

  • (arXiv 2023.3) Learning video embedding space with Natural Language Supervision, [Paper]

  • (arXiv 2023.3) Audio Visual Language Maps for Robot Navigation, [Paper], [Project]

  • (arXiv 2023.3) ViperGPT: Visual Inference via Python Execution for Reasoning, [Paper]

  • (arXiv 2023.3) ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions, [Paper], [Code]

  • (arXiv 2023.3) Can an Embodied Agent Find Your “Cat-shaped Mug”? LLM-Based Zero-Shot Object Navigation, [Paper], [Project]

  • (arXiv 2023.3) Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models, [Paper], [Code]

  • (arXiv 2023.3) PaLM-E: An Embodied Multimodal Language Model, [Paper], [Project]

  • (arXiv 2023.3) Language Is Not All You Need: Aligning Perception with Language Models, [Paper], [Code]

2023.2

  • (arXiv 2023.2) ChatGPT for Robotics: Design Principles and Model Abilities, , [Paper], [Code]

  • (arXiv 2023.2) Internet Explorer: Targeted Representation Learning on the Open Web, [Paper], [Project]

2022.11

  • (arXiv 2022.11) Visual Programming: Compositional visual reasoning without training, [Paper], [Project]

2022.7

  • (arXiv 2022.7) Language Models are General-Purpose Interfaces, [Paper], [Code]

  • (arXiv 2022.7) LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action, [Paper], [Project]

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