Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1541–1550 of 2167 papers

Title	Date	Tasks	Status	Hype
Understanding the Role of Scene Graphs in Visual Question Answering	Jan 14, 2021	Graph GenerationQuestion Answering	—Unverified	0
Predicting Relative Depth between Objects from Semantic Features	Jan 12, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Self Supervision for Attention Networks	Jan 6, 2021	image-classificationImage Classification	CodeCode Available	0
Transformers in Vision: A Survey	Jan 4, 2021	Action RecognitionActivity Recognition	—Unverified	0
Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning	Jan 1, 2021	Graph AttentionImage Captioning	—Unverified	0
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images	Jan 1, 2021	AttributeMultiple Instance Learning	CodeCode Available	1
Pano-AVQA: Grounded Audio-Visual Question Answering on 360deg Videos	Jan 1, 2021	Audio-visual Question AnsweringQuestion Answering	CodeCode Available	1
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding	Jan 1, 2021	Phrase GroundingQuestion Answering	CodeCode Available	2
TRAR: Routing the Attention Spans in Transformer for Visual Question Answering	Jan 1, 2021	Question AnsweringReferring Expression	CodeCode Available	1
Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment	Jan 1, 2021	Domain AdaptationUnsupervised Domain Adaptation	CodeCode Available	1

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All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified