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 1121–1130 of 2167 papers

Title	Date	Tasks	Status
Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts	Dec 1, 2023	Chart Question AnsweringDocument AI	—Unverified
Zero-Shot Video Question Answering with Procedural Programs	Dec 1, 2023	Code GenerationLanguage Modeling	—Unverified
Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering	Nov 29, 2023	Common Sense ReasoningQuestion Answering	—Unverified
The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation	Nov 28, 2023	DiversityQuestion Answering	—Unverified
Fully Authentic Visual Question Answering Dataset from Online Communities	Nov 27, 2023	Question AnsweringVisual Question Answering	CodeCode Available
From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation	Nov 21, 2023	Explanation GenerationVisual Question Answering (VQA)	—Unverified
KNVQA: A Benchmark for evaluation knowledge-based VQA	Nov 21, 2023	HallucinationObject Hallucination	—Unverified
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions	Nov 20, 2023	Question AnsweringVisual Question Answering	CodeCode Available
Understanding and Mitigating Classification Errors Through Interpretable Token Patterns	Nov 18, 2023	ClassificationNER	—Unverified
Attribute Diversity Determines the Systematicity Gap in VQA	Nov 15, 2023	AttributeDiagnostic	CodeCode Available

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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