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 591–600 of 2167 papers

Title	Date	Tasks	Status
VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning	Jul 17, 2025	Language ModelingLanguage Modelling	CodeCode Available
MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM	Jul 16, 2025	AttributeFace Swapping	—Unverified
Evaluating Attribute Confusion in Fashion Text-to-Image Generation	Jul 9, 2025	Attributecross-modal alignment	—Unverified
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation	Jul 9, 2025	Question AnsweringVisual Question Answering	—Unverified
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images	Jun 26, 2025	document understandingOptical Character Recognition (OCR)	CodeCode Available
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning	Jun 26, 2025	In-Context LearningMedical Visual Question Answering	—Unverified
Bridging Video Quality Scoring and Justification via Large Multimodal Models	Jun 26, 2025	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction	Jun 25, 2025	BenchmarkingPerson Identification	CodeCode Available
FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering	Jun 25, 2025	Question AnsweringVisual Question Answering	—Unverified
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning	Jun 22, 2025	Answer GenerationDecision Making	—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