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 61–70 of 2167 papers

Title	Date	Tasks	Status	Hype
GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance	May 25, 2025	Caption GenerationQuestion Answering	—Unverified	0
NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results	May 25, 2025	validVideo Quality Assessment	CodeCode Available	0
Medical Large Vision Language Models with Multi-Image Visual Ability	May 25, 2025	Question AnsweringVisual Question Answering (VQA)	CodeCode Available	0
SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and Verifiable Rewards	May 25, 2025	Image CaptioningMultimodal Reasoning	CodeCode Available	1
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning	May 24, 2025	Visual Question Answering (VQA)	—Unverified	0
MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning	May 22, 2025	DiagnosticVisual Question Answering (VQA)	—Unverified	0
A Causal Approach to Mitigate Modality Preference Bias in Medical Visual Question Answering	May 22, 2025	counterfactualMedical Visual Question Answering	—Unverified	0
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation	May 22, 2025	HallucinationImage Captioning	—Unverified	0
CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering	May 22, 2025	Computed Tomography (CT)Question Answering	—Unverified	0
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge	May 22, 2025	Anomaly DetectionQuestion 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	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