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 771–780 of 2167 papers

Title	Date	Tasks	Status
Connecting Language and Vision to Actions	Jul 1, 2018	Image CaptioningLanguage Modeling	—Unverified
Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations	Jan 4, 2025	DecoderVisual Question Answering (VQA)	—Unverified
A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 2021	Jun 24, 2021	Visual Question Answering (VQA)	—Unverified
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos	Apr 25, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision	Aug 1, 2020	Question AnsweringVisual Question Answering	—Unverified
Grounding Complex Navigational Instructions Using Scene Graphs	Jun 3, 2021	Question Answeringreinforcement-learning	—Unverified
Grounding Chest X-Ray Visual Question Answering with Generated Radiology Reports	May 22, 2025	Answer GenerationQuestion Answering	—Unverified
Convolutional Neural Networks for Video Quality Assessment	Sep 26, 2018	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning	Jan 1, 2021	Graph AttentionImage Captioning	—Unverified
Grounding Answers for Visual Questions Asked by Visually Impaired People	Jun 20, 2022	Question AnsweringVisual Question Answering	—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