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 1311–1320 of 2167 papers

Title	Date	Tasks	Status
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs	Dec 3, 2024	Image CaptioningQuantization	—Unverified
Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness	Jul 2, 2024	Image CaptioningQuestion Answering	—Unverified
Chain of Reasoning for Visual Question Answering	Dec 1, 2018	ObjectQuestion Answering	—Unverified
Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset	Oct 8, 2020	Question AnsweringVisual Question Answering	—Unverified
Characterizing Misclassifications of Deep NLP Models	Mar 12, 2021	named-entity-recognitionNamed Entity Recognition	—Unverified
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations	Sep 27, 2024	Chart Question AnsweringQuestion Answering	—Unverified
ChatBEV: A Visual Language Model that Understands BEV Maps	Mar 18, 2025	Autonomous DrivingLanguage Modeling	—Unverified
ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models	Feb 27, 2025	Person Re-IdentificationPerson Retrieval	—Unverified
ChitroJera: A Regionally Relevant Visual Question Answering Dataset for Bangla	Oct 19, 2024	Question AnsweringVisual Question Answering	—Unverified
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads	Apr 30, 2021	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