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 381–390 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?	Feb 23, 2023	Open-Domain Question AnsweringQuestion Answering	CodeCode Available	1	5
A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge	Jun 3, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Detecting Hate Speech in Multi-modal Memes	Dec 29, 2020	Binary ClassificationHate Speech Detection	CodeCode Available	1	5
Language-Informed Visual Concept Learning	Dec 6, 2023	DisentanglementNovel Concepts	CodeCode Available	1	5
Describe Anything Model for Visual Question Answering on Text-rich Images	Jul 16, 2025	DescriptiveLanguage Modeling	CodeCode Available	1	5
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos	Aug 31, 2022	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1	5
Generative Bias for Robust Visual Question Answering	Aug 1, 2022	Knowledge DistillationQuestion Answering	CodeCode Available	1	5
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images	Jan 1, 2021	AttributeMultiple Instance Learning	CodeCode Available	1	5
Multimodal fusion of imaging and genomics for lung cancer recurrence prediction	Feb 5, 2020	Computed Tomography (CT)Question Answering	CodeCode Available	1	5
Can I Trust Your Answer? Visually Grounded Video Question Answering	Sep 4, 2023	Grounded Video Question AnsweringQuestion Answering	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