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 401–410 of 2167 papers

Title	Date	Tasks	Status	Hype
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology	Sep 21, 2024	BenchmarkingDepth Estimation	—Unverified	0
JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images	Sep 19, 2024	HallucinationImage Captioning	CodeCode Available	0
Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering	Sep 19, 2024	HallucinationHallucination Evaluation	CodeCode Available	1
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution	Sep 18, 2024	Natural Language Visual Grounding	CodeCode Available	11
Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis	Sep 17, 2024	In-Context LearningQuestion Answering	—Unverified	0
Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs	Sep 17, 2024	Question AnsweringToken Reduction	CodeCode Available	1
CAST: Cross-modal Alignment Similarity Test for Vision Language Models	Sep 17, 2024	cross-modal alignmentQuestion Answering	CodeCode Available	0
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge Types	Sep 14, 2024	Language ModelingLanguage Modelling	CodeCode Available	0
QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems	Sep 14, 2024	Question AnsweringVideo Question Answering	—Unverified	0
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering	Sep 11, 2024	Question AnsweringVisual Question 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	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