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 861–870 of 2167 papers

Title	Date	Tasks	Status
Goal-Oriented Semantic Communication for Wireless Visual Question Answering	Nov 3, 2024	Edge-computingQuestion Answering	—Unverified
Right this way: Can VLMs Guide Us to See More to Answer Questions?	Nov 1, 2024	Question AnsweringVisual Question Answering	CodeCode Available
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP	Oct 31, 2024	Image CaptioningPrompt Learning	—Unverified
SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Dataset	Oct 30, 2024	Question AnsweringVisual Question Answering	—Unverified
Are VLMs Really Blind	Oct 29, 2024	Language ModelingLanguage Modelling	CodeCode Available
Improving Generalization in Visual Reasoning via Self-Ensemble	Oct 28, 2024	Visual Question Answering (VQA)Visual Reasoning	—Unverified
AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?	Oct 28, 2024	BenchmarkingQuestion Answering	CodeCode Available
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!	Oct 28, 2024	DenoisingQuestion Answering	—Unverified
Few-Shot Multimodal Explanation for Visual Question Answering	Oct 28, 2024	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available
Efficient Bilinear Attention-based Fusion for Medical Visual Question Answering	Oct 28, 2024	Computational EfficiencyDecision 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