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 1791–1800 of 2167 papers

Title	Date	Tasks	Status
Multimodal Reranking for Knowledge-Intensive Visual Question Answering	Jul 17, 2024	Answer GenerationQuestion Answering	—Unverified
Multi-Modal Retrieval Augmentation for Open-Ended and Knowledge-Intensive Video Question Answering	Feb 17, 2025	Multiple-choiceQuestion Answering	—Unverified
Multimodal Unified Attention Networks for Vision-and-Language Interactions	Aug 12, 2019	Question AnsweringVisual Grounding	—Unverified
Multiple-Question Multiple-Answer Text-VQA	Nov 15, 2023	DecoderDenoising	—Unverified
Multi-Prompts Learning with Cross-Modal Alignment for Attribute-based Person Re-Identification	Dec 28, 2023	Attributecross-modal alignment	—Unverified
Multi-task Learning of Hierarchical Vision-Language Representation	Dec 3, 2018	Multi-Task LearningQuestion Answering	—Unverified
MUST-VQA: MUltilingual Scene-text VQA	Sep 14, 2022	Question AnsweringVisual Question Answering	—Unverified
MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering	Jul 7, 2021	Medical Visual Question AnsweringMissing Labels	—Unverified
NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples	Oct 18, 2024	AttributeQuestion Answering	—Unverified
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey	Nov 26, 2024	Natural Language UnderstandingQuestion 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