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 811–820 of 2167 papers

Title	Date	Tasks	Status
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering	Dec 19, 2024	Contrastive LearningLanguage Modeling	CodeCode Available
OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization	Dec 19, 2024	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
What makes a good metric? Evaluating automatic metrics for text-to-image consistency	Dec 18, 2024	SensitivityVisual Question Answering (VQA)	—Unverified
Optimizing Vision-Language Interactions Through Decoder-Only Models	Dec 14, 2024	DecoderImage Captioning	—Unverified
Selective State Space Memory for Large Vision-Language Models	Dec 13, 2024	MambaVisual Question Answering (VQA)	—Unverified
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation	Dec 13, 2024	Instruction FollowingQuestion Answering	—Unverified
Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions	Dec 11, 2024	BenchmarkingQuestion Answering	CodeCode Available
Can We Generate Visual Programs Without Prompting LLMs?	Dec 11, 2024	Data AugmentationQuestion Answering	—Unverified
Verb Mirage: Unveiling and Assessing Verb Concept Hallucinations in Multimodal Large Language Models	Dec 6, 2024	HallucinationOptical Character Recognition (OCR)	—Unverified
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling	Dec 6, 2024	document understandingHallucination	—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