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 741–750 of 2167 papers

Title	Date	Tasks	Status
Talking to the brain: Using Large Language Models as Proxies to Model Brain Semantic Representation	Feb 26, 2025	Question Answeringvalid	—Unverified
FilterRAG: Zero-Shot Informed Retrieval-Augmented Generation to Mitigate Hallucinations in VQA	Feb 25, 2025	Question AnsweringRetrieval	—Unverified
Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines	Feb 23, 2025	Answer GenerationLanguage Modeling	—Unverified
Directional Gradient Projection for Robust Fine-Tuning of Foundation Models	Feb 21, 2025	image-classificationImage Classification	—Unverified
Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence Modeling	Feb 20, 2025	DecoderGPU	CodeCode Available
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison	Feb 20, 2025	DiversityLanguage Modeling	—Unverified
Hardware-Friendly Static Quantization Method for Video Diffusion Transformers	Feb 20, 2025	QuantizationVideo Generation	—Unverified
Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning	Feb 19, 2025	Autonomous DrivingBench2Drive	—Unverified
PitVQA++: Vector Matrix-Low-Rank Adaptation for Open-Ended Visual Question Answering in Pituitary Surgery	Feb 19, 2025	Question AnsweringVisual Question Answering	CodeCode Available
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning	Feb 18, 2025	Machine UnlearningVisual Question Answering (VQA)	—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