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

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Showing 751–760 of 2167 papers

Title	Date	Tasks	Status
Multi-Modal Retrieval Augmentation for Open-Ended and Knowledge-Intensive Video Question Answering	Feb 17, 2025	Multiple-choiceQuestion Answering	—Unverified
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
VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models	Feb 14, 2025	Image CaptioningLarge Language Model	—Unverified
Abduction of Domain Relationships from Data for VQA	Feb 13, 2025	Question AnsweringVisual Question Answering	—Unverified
Visual Graph Question Answering with ASP and LLMs for Language Parsing	Feb 13, 2025	Graph Question AnsweringOptical Character Recognition	—Unverified
Exploring the Potential of Encoder-free Architectures in 3D LMMs	Feb 13, 2025	Inductive BiasVisual Question Answering (VQA)	CodeCode Available
EmoAssist: Emotional Assistant for Visual Impairment Community	Feb 13, 2025	Emotional IntelligenceQuestion Answering	—Unverified
ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images	Feb 9, 2025	Clinical KnowledgeMedical Visual Question Answering	CodeCode Available
Performance Analysis of Traditional VQA Models Under Limited Computational Resources	Feb 9, 2025	Question AnsweringVisual Question Answering	—Unverified
Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment	Feb 7, 2025	DiversityHuman-Object Interaction Detection	—Unverified

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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