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 461–470 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection	Jul 26, 2022	DecoderKnowledge Graphs	CodeCode Available	1	5
Describe Anything Model for Visual Question Answering on Text-rich Images	Jul 16, 2025	DescriptiveLanguage Modeling	CodeCode Available	1	5
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering	Nov 13, 2023	Decision MakingExplanation Generation	CodeCode Available	1	5
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA	Oct 10, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images	Oct 1, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Detecting and Preventing Hallucinations in Large Vision Language Models	Aug 11, 2023	16kHallucination	CodeCode Available	1	5
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering	Jun 29, 2023	Answer GenerationQuestion Answering	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
LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering	Nov 21, 2020	Answer GenerationQuestion Answering	CodeCode Available	1	5
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models	Mar 12, 2024	Concept AlignmentInstruction Following	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