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 231–240 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering	Nov 13, 2023	Decision MakingExplanation Generation	CodeCode Available	1	5
Deep Multimodal Neural Architecture Search	Apr 25, 2020	DecoderImage-text matching	CodeCode Available	1	5
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling	Sep 4, 2022	Fill MaskOptical Flow Estimation	CodeCode Available	1	5
Interpreting Chest X-rays Like a Radiologist: A Benchmark with Clinical Reasoning	May 29, 2025	DiagnosticQuestion Answering	CodeCode Available	1	5
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA	Sep 10, 2021	Image CaptioningQuestion Answering	CodeCode Available	1	5
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models	Mar 28, 2024	HallucinationQuestion Answering	CodeCode Available	1	5
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering	Apr 7, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1	5
An Empirical Study of Multimodal Model Merging	Apr 28, 2023	modelRetrieval	CodeCode Available	1	5
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering	Apr 20, 2021	Graph Neural NetworkGraph Question Answering	CodeCode Available	1	5
Greedy Gradient Ensemble for Robust Visual Question Answering	Jul 27, 2021	Question AnsweringVisual Question Answering	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	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