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 191–200 of 2167 papers

Title	Date	Tasks	Status	Hype
PitVQA++: Vector Matrix-Low-Rank Adaptation for Open-Ended Visual Question Answering in Pituitary Surgery	Feb 19, 2025	Question AnsweringVisual Question Answering	CodeCode Available	0
Qwen2.5-VL Technical Report	Feb 19, 2025	document understanding	CodeCode Available	11
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning	Feb 18, 2025	Machine UnlearningVisual Question Answering (VQA)	—Unverified	0
Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization	Feb 18, 2025	Image RetrievalQuestion Answering	CodeCode Available	2
Multi-Modal Retrieval Augmentation for Open-Ended and Knowledge-Intensive Video Question Answering	Feb 17, 2025	Multiple-choiceQuestion Answering	—Unverified	0
MMUnlearner: Reformulating Multimodal Machine Unlearning in the Era of Multimodal Large Language Models	Feb 16, 2025	Language ModelingLanguage Modelling	CodeCode Available	1
USER-VLM 360: Personalized Vision Language Models with User-aware Tuning for Social Human-Robot Interactions	Feb 15, 2025	Multimodal ReasoningVisual Question Answering (VQA)	CodeCode Available	0
VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models	Feb 14, 2025	Image CaptioningLarge Language Model	—Unverified	0
Exploring the Potential of Encoder-free Architectures in 3D LMMs	Feb 13, 2025	Inductive BiasVisual Question Answering (VQA)	CodeCode Available	0
Abduction of Domain Relationships from Data for VQA	Feb 13, 2025	Question AnsweringVisual Question Answering	—Unverified	0

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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