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 311–320 of 2167 papers

Title	Date	Tasks	Status	Hype
LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement	Nov 20, 2024	Autonomous DrivingComputational Efficiency	—Unverified	0
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model	Nov 19, 2024	Language ModelingLanguage Modelling	—Unverified	0
Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media Contexts	Nov 18, 2024	BenchmarkingMultimodal Large Language Model	CodeCode Available	0
F^3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics	Nov 17, 2024	DiversityFederated Learning	—Unverified	0
Understanding Multimodal LLMs: the Mechanistic Interpretability of Llava in Visual Question Answering	Nov 17, 2024	HallucinationIn-Context Learning	CodeCode Available	0
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry	Nov 17, 2024	Question AnsweringScene Understanding	—Unverified	0
A Comprehensive Survey on Visual Question Answering Datasets and Algorithms	Nov 17, 2024	DiagnosticMiscellaneous	—Unverified	0
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts	Nov 16, 2024	Mixture-of-ExpertsOptical Character Recognition (OCR)	CodeCode Available	1
Visual question answering based evaluation metrics for text-to-image generation	Nov 15, 2024	Image GenerationImage Manipulation	—Unverified	0
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding	Nov 12, 2024	document understandingOptical Character Recognition (OCR)	—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