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 951–960 of 2167 papers

Title	Date	Tasks	Status
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model	Oct 3, 2024	image-classificationImage Classification	—Unverified
LOIS: Looking Out of Instance Semantics for Visual Question Answering	Jul 26, 2023	Question AnsweringVisual Question Answering	—Unverified
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images	Nov 23, 2022	Optical Character Recognition (OCR)Question Answering	—Unverified
Language Models are General-Purpose Interfaces	Jun 13, 2022	Causal Language ModelingFew-Shot Learning	—Unverified
Making Video Quality Assessment Models Robust to Bit Depth	Apr 25, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Gender and Racial Bias in Visual Question Answering Datasets	May 17, 2022	Question AnsweringVisual Question Answering	—Unverified
Gemini Pro Defeated by GPT-4V: Evidence from Education	Dec 27, 2023	image-classificationImage Classification	—Unverified
COCO is "ALL'' You Need for Visual Instruction Fine-tuning	Jan 17, 2024	AllImage Captioning	—Unverified
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources	Nov 22, 2015	FormGeneral Knowledge	—Unverified
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning	Jun 22, 2025	Answer GenerationDecision Making	—Unverified

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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