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 241–250 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
How Much Can CLIP Benefit Vision-and-Language Tasks?	Jul 13, 2021	Question AnsweringVision and Language Navigation	CodeCode Available	1	5
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering	Dec 14, 2021	Graph MatchingQuestion Answering	CodeCode Available	1	5
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning	May 10, 2021	Arithmetic ReasoningGeometry Problem Solving	CodeCode Available	1	5
Learning to Answer Visual Questions from Web Videos	May 10, 2022	Dataset GenerationQuestion Answering	CodeCode Available	1	5
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases	Sep 9, 2019	Natural Language InferenceQuestion Answering	CodeCode Available	1	5
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA	Oct 10, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer	Feb 18, 2021	DecoderDocument Image Classification	CodeCode Available	1	5
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception	Aug 19, 2021	Action RecognitionImage Quality Assessment	CodeCode Available	1	5
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs	Mar 2, 2023	ArticlesMedical Visual Question Answering	CodeCode Available	1	5
Attention-Based Context Aware Reasoning for Situation Recognition	Jun 1, 2020	Action RecognitionFine-grained Action Recognition	CodeCode Available	1	5

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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