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 351–360 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder	Jun 28, 2025	Image SegmentationLarge Language Model	CodeCode Available	1	5
Detecting Hate Speech in Multi-modal Memes	Dec 29, 2020	Binary ClassificationHate Speech Detection	CodeCode Available	1	5
Debiasing Multimodal Models via Causal Information Minimization	Nov 28, 2023	Visual Question Answering (VQA)	CodeCode Available	1	5
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding	Oct 12, 2022	document-image-classificationDocument Image Classification	CodeCode Available	1	5
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge	May 31, 2019	object-detectionObject Detection	CodeCode Available	1	5
Declaration-based Prompt Tuning for Visual Question Answering	May 5, 2022	Image-text matchingLanguage Modeling	CodeCode Available	1	5
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models	Mar 28, 2024	HallucinationQuestion Answering	CodeCode Available	1	5
Combo of Thinking and Observing for Outside-Knowledge VQA	May 10, 2023	DecoderQuestion Answering	CodeCode Available	1	5
Kosmos-2: Grounding Multimodal Large Language Models to the World	Jun 26, 2023	Image CaptioningIn-Context Learning	CodeCode Available	1	5
LaTr: Layout-Aware Transformer for Scene-Text VQA	Dec 23, 2021	Optical Character Recognition (OCR)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