MM Grounding DINO

An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

Abstract

Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC). Its effectiveness has led to its widespread adoption as a mainstream architecture for various downstream applications. However, despite its significance, the original Grounding-DINO model lacks comprehensive public technical details due to the unavailability of its training code. To bridge this gap, we present MM-Grounding-DINO, an open-source, comprehensive, and user-friendly baseline, which is built with the MMDetection toolbox. It adopts abundant vision datasets for pre-training and various detection and grounding datasets for fine-tuning. We give a comprehensive analysis of each reported result and detailed settings for reproduction. The extensive experiments on the benchmarks mentioned demonstrate that our MM-Grounding-DINO-Tiny outperforms the Grounding-DINO-Tiny baseline. We release all our models to the research community.

Dataset Preparation

Please refer to dataset_prepare.md or 中文版数据准备

✨ What's New

💎 We have released the pre-trained weights for Swin-B and Swin-L, welcome to try and give feedback.

Usage

Please refer to usage.md or 中文版用法说明

Zero-Shot COCO Results and Models

Model	Backbone	Style	COCO mAP	Pre-Train Data	Config	Download
GDINO-T	Swin-T	Zero-shot	46.7	O365
GDINO-T	Swin-T	Zero-shot	48.1	O365,GoldG
GDINO-T	Swin-T	Zero-shot	48.4	O365,GoldG,Cap4M	config	model
MM-GDINO-T	Swin-T	Zero-shot	48.5(+1.8)	O365	config
MM-GDINO-T	Swin-T	Zero-shot	50.4(+2.3)	O365,GoldG	config	model \| log
MM-GDINO-T	Swin-T	Zero-shot	50.5(+2.1)	O365,GoldG,GRIT	config	model \| log
MM-GDINO-T	Swin-T	Zero-shot	50.6(+2.2)	O365,GoldG,V3Det	config	model \| log
MM-GDINO-T	Swin-T	Zero-shot	50.4(+2.0)	O365,GoldG,GRIT,V3Det	config	model \| log
MM-GDINO-B	Swin-B	Zero-shot	52.5	O365,GoldG,V3Det	config	model \| log
MM-GDINO-B*	Swin-B	-	59.5	O365,ALL	config	model \| log
MM-GDINO-L	Swin-L	Zero-shot	53.0	O365V2,OpenImageV6,GoldG	config	model \| log
MM-GDINO-L*	Swin-L	-	60.3	O365V2,OpenImageV6,ALL	config	model \| log

This * indicates that the model has not been fully trained yet. We will release the final weights in the future.
ALL: GoldG,V3det,COCO2017,LVISV1,COCO2014,GRIT,RefCOCO,RefCOCO+,RefCOCOg,gRefCOCO.

Zero-Shot LVIS Results

Model	MiniVal APr	MiniVal APc	MiniVal APf	MiniVal AP	Val1.0 APr	Val1.0 APc	Val1.0 APf	Val1.0 AP	Pre-Train Data
GDINO-T	18.8	24.2	34.7	28.8	10.1	15.3	29.9	20.1	O365,GoldG,Cap4M
MM-GDINO-T	28.1	30.2	42.0	35.7(+6.9)	17.1	22.4	36.5	27.0(+6.9)	O365,GoldG
MM-GDINO-T	26.6	32.4	41.8	36.5(+7.7)	17.3	22.6	36.4	27.1(+7.0)	O365,GoldG,GRIT
MM-GDINO-T	33.0	36.0	45.9	40.5(+11.7)	21.5	25.5	40.2	30.6(+10.5)	O365,GoldG,V3Det
MM-GDINO-T	34.2	37.4	46.2	41.4(+12.6)	23.6	27.6	40.5	31.9(+11.8)	O365,GoldG,GRIT,V3Det

The MM-GDINO-T config file is mini-lvis and lvis 1.0

Zero-Shot ODinW (Object Detection in the Wild) Results

Results and models of ODinW13

Method	GDINO-T (O365,GoldG,Cap4M)	MM-GDINO-T (O365,GoldG)	MM-GDINO-T (O365,GoldG,GRIT)	MM-GDINO-T (O365,GoldG,V3Det)	MM-GDINO-T (O365,GoldG,GRIT,V3Det)
AerialMaritimeDrone	0.173	0.133	0.155	0.177	0.151
Aquarium	0.195	0.252	0.261	0.266	0.283
CottontailRabbits	0.799	0.771	0.810	0.778	0.786
EgoHands	0.608	0.499	0.537	0.506	0.519
NorthAmericaMushrooms	0.507	0.331	0.462	0.669	0.767
Packages	0.687	0.707	0.687	0.710	0.706
PascalVOC	0.563	0.565	0.580	0.556	0.566
pistols	0.726	0.585	0.709	0.671	0.729
pothole	0.215	0.136	0.285	0.199	0.243
Raccoon	0.549	0.469	0.511	0.553	0.535
ShellfishOpenImages	0.393	0.321	0.437	0.519	0.488
thermalDogsAndPeople	0.657	0.556	0.603	0.493	0.542
VehiclesOpenImages	0.613	0.566	0.603	0.614	0.615
Average	0.514	0.453	0.511	0.516	0.533

The MM-GDINO-T config file is odinw13

Results and models of ODinW35

Method	GDINO-T (O365,GoldG,Cap4M)	MM-GDINO-T (O365,GoldG)	MM-GDINO-T (O365,GoldG,GRIT)	MM-GDINO-T (O365,GoldG,V3Det)	MM-GDINO-T (O365,GoldG,GRIT,V3Det)
AerialMaritimeDrone_large	0.173	0.133	0.155	0.177	0.151
AerialMaritimeDrone_tiled	0.206	0.170	0.225	0.184	0.206
AmericanSignLanguageLetters	0.002	0.016	0.020	0.011	0.007
Aquarium	0.195	0.252	0.261	0.266	0.283
BCCD	0.161	0.069	0.118	0.083	0.077
boggleBoards	0.000	0.002	0.001	0.001	0.002
brackishUnderwater	0.021	0.033	0.021	0.025	0.025
ChessPieces	0.000	0.000	0.000	0.000	0.000
CottontailRabbits	0.806	0.771	0.810	0.778	0.786
dice	0.004	0.002	0.005	0.001	0.001
DroneControl	0.042	0.047	0.097	0.088	0.074
EgoHands_generic	0.608	0.527	0.537	0.506	0.519
EgoHands_specific	0.002	0.001	0.005	0.007	0.003
HardHatWorkers	0.046	0.048	0.070	0.070	0.108
MaskWearing	0.004	0.009	0.004	0.011	0.009
MountainDewCommercial	0.430	0.453	0.465	0.194	0.430
NorthAmericaMushrooms	0.471	0.331	0.462	0.669	0.767
openPoetryVision	0.000	0.001	0.000	0.000	0.000
OxfordPets_by_breed	0.003	0.002	0.004	0.006	0.004
OxfordPets_by_species	0.011	0.019	0.016	0.020	0.015
PKLot	0.001	0.004	0.002	0.008	0.007
Packages	0.695	0.707	0.687	0.710	0.706
PascalVOC	0.563	0.565	0.580	0.566	0.566
pistols	0.726	0.585	0.709	0.671	0.729
plantdoc	0.005	0.005	0.007	0.008	0.011
pothole	0.215	0.136	0.219	0.077	0.168
Raccoons	0.549	0.469	0.511	0.553	0.535
selfdrivingCar	0.089	0.091	0.076	0.094	0.083
ShellfishOpenImages	0.393	0.321	0.437	0.519	0.488
ThermalCheetah	0.087	0.063	0.081	0.030	0.045
thermalDogsAndPeople	0.657	0.556	0.603	0.493	0.543
UnoCards	0.006	0.012	0.010	0.009	0.005
VehiclesOpenImages	0.613	0.566	0.603	0.614	0.615
WildfireSmoke	0.134	0.106	0.154	0.042	0.127
websiteScreenshots	0.012	0.02	0.016	0.016	0.016
Average	0.227	0.202	0.228	0.214	0.284

The MM-GDINO-T config file is odinw35

Zero-Shot Referring Expression Comprehension Results

Method	GDINO-T (O365,GoldG,Cap4M)	MM-GDINO-T (O365,GoldG)	MM-GDINO-T (O365,GoldG,GRIT)	MM-GDINO-T (O365,GoldG,V3Det)	MM-GDINO-T (O365,GoldG,GRIT,V3Det)
RefCOCO val @1,5,10	50.8/89.5/94.9	53.1/89.9/94.7	53.4/90.3/95.5	52.1/89.8/95.0	53.1/89.7/95.1
RefCOCO testA @1,5,10	57.4/91.3/95.6	59.7/91.5/95.9	58.8/91.70/96.2	58.4/86.8/95.6	59.1/91.0/95.5
RefCOCO testB @1,5,10	45.0/86.5/92.9	46.4/86.9/92.2	46.8/87.7/93.3	45.4/86.2/92.6	46.8/87.8/93.6
RefCOCO+ val @1,5,10	51.6/86.4/92.6	53.1/87.0/92.8	53.5/88.0/93.7	52.5/86.8/93.2	52.7/87.7/93.5
RefCOCO+ testA @1,5,10	57.3/86.7/92.7	58.9/87.3/92.9	59.0/88.1/93.7	58.1/86.7/93.5	58.7/87.2/93.1
RefCOCO+ testB @1,5,10	46.4/84.1/90.7	47.9/84.3/91.0	47.9/85.5/92.7	46.9/83.7/91.5	48.4/85.8/92.1
RefCOCOg val @1,5,10	60.4/92.1/96.2	61.2/92.6/96.1	62.7/93.3/97.0	61.7/92.9/96.6	62.9/93.3/97.2
RefCOCOg test @1,5,10	59.7/92.1/96.3	61.1/93.3/96.7	62.6/94.9/97.1	61.0/93.1/96.8	62.9/93.9/97.4

Method	thresh_score	GDINO-T (O365,GoldG,Cap4M)	MM-GDINO-T (O365,GoldG)	MM-GDINO-T (O365,GoldG,GRIT)	MM-GDINO-T (O365,GoldG,V3Det)	MM-GDINO-T (O365,GoldG,GRIT,V3Det)
gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc	0.5	39.3/70.4				39.4/67.5
gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc	0.6	40.5/83.8				40.6/83.1
gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc	0.7	41.3/91.8	39.8/84.7	40.7/89.7	40.3/88.8	41.0/91.3
gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc	0.8	41.5/96.8				41.1/96.4
gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc	0.5	31.9/70.4				33.1/69.5
gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc	0.6	29.3/82.9				29.2/84.3
gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc	0.7	27.2/90.2	26.3/89.0	26.0/91.9	25.4/91.8	26.1/93.0
gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc	0.8	25.1/96.3				23.8/97.2
gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc	0.5	30.9/72.5				33.0/69.6
gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc	0.6	30.0/86.1				31.6/96.7
gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc	0.7	29.7/93.5	31.3/84.8	30.6/90.2	30.7/89.9	30.4/92.3
gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc	0.8	29.1/97.4				29.5/84.2

The MM-GDINO-T config file is here

Zero-Shot Description Detection Dataset(DOD)

pip install ddd-dataset

Method	mode	GDINO-T (O365,GoldG,Cap4M)	MM-GDINO-T (O365,GoldG)	MM-GDINO-T (O365,GoldG,GRIT)	MM-GDINO-T (O365,GoldG,V3Det)	MM-GDINO-T (O365,GoldG,GRIT,V3Det)
FULL/short/middle/long/very long	concat	17.2/18.0/18.7/14.8/16.3	15.6/17.3/16.7/14.3/13.1	17.0/17.7/18.0/15.7/15.7	16.2/17.4/16.8/14.9/15.4	17.5/23.4/18.3/14.7/13.8
FULL/short/middle/long/very long	parallel	22.3/28.2/24.8/19.1/13.9	21.7/24.7/24.0/20.2/13.7	22.5/25.6/25.1/20.5/14.9	22.3/25.6/24.5/20.6/14.7	22.9/28.1/25.4/20.4/14.4
PRES/short/middle/long/very long	concat	17.8/18.3/19.2/15.2/17.3	16.4/18.4/17.3/14.5/14.2	17.9/19.0/18.3/16.5/17.5	16.6/18.8/17.1/15.1/15.0	18.0/23.7/18.6/15.4/13.3
PRES/short/middle/long/very long	parallel	21.0/27.0/22.8/17.5/12.5	21.3/25.5/22.8/19.2/12.9	21.5/25.2/23.0/19.0/15.0	21.6/25.7/23.0/19.5/14.8	21.9/27.4/23.2/19.1/14.2
ABS/short/middle/long/very long	concat	15.4/17.1/16.4/13.6/14.9	13.4/13.4/14.5/13.5/11.9	14.5/13.1/16.7/13.6/13.3	14.8/12.5/15.6/14.3/15.8	15.9/22.2/17.1/12.5/14.4
ABS/short/middle/long/very long	parallel	26.0/32.0/33.0/23.6/15.5	22.8/22.2/28.7/22.9/14.7	25.6/26.8/33.9/24.5/14.7	24.1/24.9/30.7/23.8/14.7	26.0/30.3/34.1/23.9/14.6

Note:

Considering that the evaluation time for Inter-scenario is very long and the performance is low, it is temporarily not supported. The mentioned metrics are for Intra-scenario.
concat is the default inference mode for Grounding DINO, where it concatenates multiple sub-sentences with "." to form a single sentence for inference. On the other hand, "parallel" performs inference on each sub-sentence in a for-loop.
The MM-GDINO-T config file is concat_dod and parallel_dod

Pretrain Flickr30k Results

Model	Pre-Train Data	Val R@1	Val R@5	Val R@10	Test R@1	Test R@5	Test R@10
GLIP-T	O365,GoldG	84.9	94.9	96.3	85.6	95.4	96.7
GLIP-T	O365,GoldG,CC3M,SBU	85.3	95.5	96.9	86.0	95.9	97.2
GDINO-T	O365,GoldG,Cap4M	87.8	96.6	98.0	88.1	96.9	98.2
MM-GDINO-T	O365,GoldG	85.5	95.6	97.2	86.2	95.7	97.4
MM-GDINO-T	O365,GoldG,GRIT	86.7	95.8	97.6	87.0	96.2	97.7
MM-GDINO-T	O365,GoldG,V3Det	85.9	95.7	97.4	86.3	95.7	97.4
MM-GDINO-T	O365,GoldG,GRIT,V3Det	86.7	96.0	97.6	87.2	96.2	97.7

Note:

@1,5,10 refers to precision at the top 1, 5, and 10 positions in a predicted ranked list.
The MM-GDINO-T config file is here

Validating the generalization of a pre-trained model through fine-tuning

RTTS

Architecture	Backbone	Lr schd	box AP
Faster R-CNN	R-50	1x	48.1
Cascade R-CNN	R-50	1x	50.8
ATSS	R-50	1x	48.2
TOOD	R-50	1X	50.8
MM-GDINO(zero-shot)	Swin-T		49.8
MM-GDINO	Swin-T	1x	69.1

The reference metrics come from https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/rtts_dataset
The MM-GDINO-T config file is here

RUOD

Architecture	Backbone	Lr schd	box AP
Faster R-CNN	R-50	1x	52.4
Cascade R-CNN	R-50	1x	55.3
ATSS	R-50	1x	55.7
TOOD	R-50	1X	57.4
MM-GDINO(zero-shot)	Swin-T		29.8
MM-GDINO	Swin-T	1x	65.5

The reference metrics come from https://github.com/BIGWangYuDong/lqit/tree/main/configs/detection/ruod_dataset
The MM-GDINO-T config file is here

Brain Tumor

Architecture	Backbone	Lr schd	box AP
Faster R-CNN	R-50	50e	43.5
Cascade R-CNN	R-50	50e	46.2
DINO	R-50	50e	46.4
Cascade-DINO	R-50	50e	48.6
MM-GDINO	Swin-T	50e	47.5

The reference metrics come from https://arxiv.org/abs/2307.11035
The MM-GDINO-T config file is here

Cityscapes

Architecture	Backbone	Lr schd	box AP
Faster R-CNN	R-50	50e	30.1
Cascade R-CNN	R-50	50e	31.8
DINO	R-50	50e	34.5
Cascade-DINO	R-50	50e	34.8
MM-GDINO(zero-shot)	Swin-T		34.2
MM-GDINO	Swin-T	50e	51.5

The reference metrics come from https://arxiv.org/abs/2307.11035
The MM-GDINO-T config file is here

People in Painting

Architecture	Backbone	Lr schd	box AP
Faster R-CNN	R-50	50e	17.0
Cascade R-CNN	R-50	50e	18.0
DINO	R-50	50e	12.0
Cascade-DINO	R-50	50e	13.4
MM-GDINO(zero-shot)	Swin-T		23.1
MM-GDINO	Swin-T	50e	38.9

The reference metrics come from https://arxiv.org/abs/2307.11035
The MM-GDINO-T config file is here

COCO

(1) Closed-set performance

Architecture	Backbone	Lr schd	box AP
Faster R-CNN	R-50	1x	37.4
Cascade R-CNN	R-50	1x	40.3
ATSS	R-50	1x	39.4
TOOD	R-50	1X	42.4
DINO	R-50	1X	50.1
GLIP(zero-shot)	Swin-T		46.6
GDINO(zero-shot)	Swin-T		48.5
MM-GDINO(zero-shot)	Swin-T		50.4
GLIP	Swin-T	1x	55.4
GDINO	Swin-T	1x	58.1
MM-GDINO	Swin-T	1x	58.2

The MM-GDINO-T config file is here

(2) Open-set continuing pretraining performance

Architecture	Backbone	Lr schd	box AP
GLIP(zero-shot)	Swin-T		46.7
GDINO(zero-shot)	Swin-T		48.5
MM-GDINO(zero-shot)	Swin-T		50.4
MM-GDINO	Swin-T	1x	54.7

The MM-GDINO-T config file is here
Due to the small size of the COCO dataset, continuing pretraining solely on COCO can easily lead to overfitting. The results shown above are from the third epoch. I do not recommend you train using this approach.

(3) Open vocabulary performance

Architecture	Backbone	Lr schd	box AP	Base box AP	Novel box AP	box AP@50	Base box AP@50	Novel box AP@50
MM-GDINO(zero-shot)	Swin-T		51.1	48.4	58.9	66.7	64.0	74.2
MM-GDINO	Swin-T	1x	57.2	56.1	60.4	73.6	73.0	75.3

The MM-GDINO-T config file is here

LVIS 1.0

(1) Open-set continuing pretraining performance

Architecture	Backbone	Lr schd	MiniVal APr	MiniVal APc	MiniVal APf	MiniVal AP	Val1.0 APr	Val1.0 APc	Val1.0 APf	Val1.0 AP
GLIP(zero-shot)	Swin-T		18.1	21.2	33.1	26.7	10.8	14.7	29.0	19.6
GDINO(zero-shot)	Swin-T		18.8	24.2	34.7	28.8	10.1	15.3	29.9	20.1
MM-GDINO(zero-shot)	Swin-T		34.2	37.4	46.2	41.4	23.6	27.6	40.5	31.9
MM-GDINO	Swin-T	1x	50.7	58.8	60.1	58.7	45.2	50.2	56.1	51.7

The MM-GDINO-T config file is here

(2) Open vocabulary performance

Architecture	Backbone	Lr schd	MiniVal APr	MiniVal APc	MiniVal APf	MiniVal AP
MM-GDINO(zero-shot)	Swin-T		34.2	37.4	46.2	41.4
MM-GDINO	Swin-T	1x	43.2	57.4	59.3	57.1

The MM-GDINO-T config file is here

RefEXP

RefCOCO

Architecture	Backbone	Lr schd	val @1	val @5	val @10	testA @1	testA @5	testA @10	testB @1	testB @5	testB @10
GDINO(zero-shot)	Swin-T		50.8	89.5	94.9	57.5	91.3	95.6	45.0	86.5	92.9
MM-GDINO(zero-shot)	Swin-T		53.1	89.7	95.1	59.1	91.0	95.5	46.8	87.8	93.6
GDINO	Swin-T	UNK	89.2			91.9			86.0
MM-GDINO	Swin-T	5e	89.5	98.6	99.4	91.4	99.2	99.8	86.6	97.9	99.1

The MM-GDINO-T config file is here

RefCOCO+

Architecture	Backbone	Lr schd	val @1	val @5	val @10	testA @1	testA @5	testA @10	testB @1	testB @5	testB @10
GDINO(zero-shot)	Swin-T		51.6	86.4	92.6	57.3	86.7	92.7	46.4	84.1	90.7
MM-GDINO(zero-shot)	Swin-T		52.7	87.7	93.5	58.7	87.2	93.1	48.4	85.8	92.1
GDINO	Swin-T	UNK	81.1			87.4			74.7
MM-GDINO	Swin-T	5e	82.1	97.8	99.2	87.5	99.2	99.7	74.0	96.3	96.4

The MM-GDINO-T config file is here

RefCOCOg

Architecture	Backbone	Lr schd	val @1	val @5	val @10	test @1	test @5	test @10
GDINO(zero-shot)	Swin-T		60.4	92.1	96.2	59.7	92.1	96.3
MM-GDINO(zero-shot)	Swin-T		62.9	93.3	97.2	62.9	93.9	97.4
GDINO	Swin-T	UNK	84.2			84.9
MM-GDINO	Swin-T	5e	85.5	98.4	99.4	85.8	98.6	99.4

The MM-GDINO-T config file is here

gRefCOCO

Architecture	Backbone	Lr schd	val Pr@(F1=1, IoU≥0.5)	val N-acc	testA Pr@(F1=1, IoU≥0.5)	testA N-acc	testB Pr@(F1=1, IoU≥0.5)	testB N-acc
GDINO(zero-shot)	Swin-T		41.3	91.8	27.2	90.2	29.7	93.5
MM-GDINO(zero-shot)	Swin-T		41.0	91.3	26.1	93.0	30.4	92.3
MM-GDINO	Swin-T	5e	45.1	64.7	42.5	65.5	40.3	63.2

The MM-GDINO-T config file is here

Citation

If you find this project useful in your research, please consider citing:

@article{zhao2024open,
  title={An Open and Comprehensive Pipeline for Unified Object Grounding and Detection},
  author={Zhao, Xiangyu and Chen, Yicheng and Xu, Shilin and Li, Xiangtai and Wang, Xinjiang and Li, Yining and Huang, Haian},
  journal={arXiv preprint arXiv:2401.02361},
  year={2024}
}

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README.md

README.md

MM Grounding DINO

Abstract

Dataset Preparation

✨ What's New

Usage

Zero-Shot COCO Results and Models

Zero-Shot LVIS Results

Zero-Shot ODinW (Object Detection in the Wild) Results

Results and models of ODinW13

Results and models of ODinW35

Zero-Shot Referring Expression Comprehension Results

Zero-Shot Description Detection Dataset(DOD)

Pretrain Flickr30k Results

Validating the generalization of a pre-trained model through fine-tuning

RTTS

RUOD

Brain Tumor

Cityscapes

People in Painting

COCO

LVIS 1.0

RefEXP

RefCOCO

RefCOCO+

RefCOCOg

gRefCOCO

Citation

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README.md

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README.md

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MM Grounding DINO

Abstract

Dataset Preparation

✨ What's New

Usage

Zero-Shot COCO Results and Models

Zero-Shot LVIS Results

Zero-Shot ODinW (Object Detection in the Wild) Results

Results and models of ODinW13

Results and models of ODinW35

Zero-Shot Referring Expression Comprehension Results

Zero-Shot Description Detection Dataset(DOD)

Pretrain Flickr30k Results

Validating the generalization of a pre-trained model through fine-tuning

RTTS

RUOD

Brain Tumor

Cityscapes

People in Painting

COCO

LVIS 1.0

RefEXP

RefCOCO

RefCOCO+

RefCOCOg

gRefCOCO

Citation