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 1491–1500 of 2167 papers

Title	Date	Tasks	Status
Fooling Vision and Language Models Despite Localization and Attention Mechanism	Sep 25, 2017	Dense CaptioningNatural Language Understanding	—Unverified
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption	Aug 23, 2024	Instruction FollowingKnowledge Distillation	—Unverified
FOVQA: Blind Foveated Video Quality Assessment	Jun 24, 2021	Video CompressionVideo Quality Assessment	—Unverified
Free Form Medical Visual Question Answering in Radiology	Jan 23, 2024	DiagnosticForm	—Unverified
From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data	May 6, 2022	Question AnsweringVisual Question Answering	—Unverified
From Images to Textual Prompts: Zero-Shot Visual Question Answering With Frozen Large Language Models	Jan 1, 2023	Question AnsweringVisual Question Answering	—Unverified
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities	Nov 1, 2023	NavigateQuestion Answering	—Unverified
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts	Nov 30, 2018	Novel ConceptsQuestion Answering	—Unverified
From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge	Jun 10, 2025	Knowledge GraphsLanguage Modeling	—Unverified
From Pixels to Objects: Cubic Visual Attention for Visual Question Answering	Jun 4, 2022	ObjectQuestion 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