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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 301–310 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering	Apr 22, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Cross-modal Retrieval for Knowledge-based Visual Question Answering	Jan 11, 2024	Cross-Modal RetrievalQuestion Answering	CodeCode Available	1	5
Cross-Modality Relevance for Reasoning on Language and Vision	May 12, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder	Jun 28, 2025	Image SegmentationLarge Language Model	CodeCode Available	1	5
I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision	Nov 17, 2022	Image CaptioningQuestion Answering	CodeCode Available	1	5
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages	Jan 27, 2022	Cross-Modal RetrievalFew-Shot Learning	CodeCode Available	1	5
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs	Nov 27, 2023	Adversarial RobustnessVisual Question Answering (VQA)	CodeCode Available	1	5
Change Detection Meets Visual Question Answering	Dec 12, 2021	Answer GenerationChange Detection	CodeCode Available	1	5
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models	Oct 16, 2021	Image CaptioningLanguage Modeling	CodeCode Available	1	5
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers	May 27, 2023	Image CaptioningImage Retrieval	CodeCode Available	1	5

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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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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