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

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Showing 2041–2050 of 2167 papers

Title	Date	Tasks	Status
A Unified Hallucination Mitigation Framework for Large Vision-Language Models	Sep 24, 2024	HallucinationQuestion Answering	CodeCode Available
Revisiting Video Quality Assessment from the Perspective of Generalization	Sep 23, 2024	Image Quality AssessmentVideo Quality Assessment	CodeCode Available
Revisiting Visual Question Answering Baselines	Jun 27, 2016	Binary ClassificationMultiple-choice	CodeCode Available
Hallucination Benchmark in Medical Visual Question Answering	Jan 11, 2024	HallucinationMedical Visual Question Answering	CodeCode Available
HalLoc: Token-level Localization of Hallucinations for Vision Language Models	Jun 12, 2025	HallucinationImage Captioning	CodeCode Available
Copy-Move Forgery Detection and Question Answering for Remote Sensing Image	Dec 3, 2024	Question AnsweringVisual Question Answering	CodeCode Available
REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question Answering	Jul 27, 2020	Question AnsweringVisual Question Answering	CodeCode Available
Augmenting Visual Question Answering with Semantic Frame Information in a Multitask Learning Approach	Jan 31, 2020	Question AnsweringVisual Question Answering	CodeCode Available
Right this way: Can VLMs Guide Us to See More to Answer Questions?	Nov 1, 2024	Question AnsweringVisual Question Answering	CodeCode Available
Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning	May 1, 2018	Commonsense Causal ReasoningImage Captioning	CodeCode Available

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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