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 801–810 of 2167 papers

Title	Date	Tasks	Status
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark	Mar 28, 2025	Question AnsweringVisual Question Answering	—Unverified
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment	May 21, 2025	Language ModelingLanguage Modelling	—Unverified
Grounded Word Sense Translation	Jun 1, 2019	Grounded language learningMachine Translation	—Unverified
CQ-VQA: Visual Question Answering on Categorized Questions	Feb 17, 2020	Question AnsweringVisual Question Answering	—Unverified
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models	Oct 21, 2024	Instruction Followingobject-detection	—Unverified
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering	Jan 14, 2022	Generative Question AnsweringImage to text	—Unverified
`Just because you are right, doesn't mean I am wrong': Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks	Apr 1, 2021	Question AnsweringVisual Question Answering	—Unverified
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?	Jun 11, 2016	Question AnsweringVisual Question Answering	—Unverified
Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment	Feb 7, 2025	DiversityHuman-Object Interaction Detection	—Unverified
Graph-Structured Representations for Visual Question Answering	Sep 19, 2016	Multiple-choiceQuestion 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