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 2111–2120 of 2167 papers

Title	Date	Tasks	Status
Adaptively Clustering Neighbor Elements for Image-Text Generation	Jan 5, 2023	ClusteringDecoder	CodeCode Available
Zero-shot Translation of Attention Patterns in VQA Models to Natural Language	Nov 8, 2023	Image CaptioningLanguage Modeling	CodeCode Available
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions	Nov 20, 2023	Question AnsweringVisual Question Answering	CodeCode Available
Uncovering the Full Potential of Visual Grounding Methods in VQA	Jan 15, 2024	Question AnsweringVisual Grounding	CodeCode Available
Self Supervision for Attention Networks	Jan 6, 2021	image-classificationImage Classification	CodeCode Available
ArtQuest: Countering Hidden Language Biases in ArtVQA	Jan 4, 2024	Question AnsweringVisual Question Answering	CodeCode Available
Analyzing Modular Approaches for Visual Question Decomposition	Nov 10, 2023	Code GenerationVisual Question Answering (VQA)	CodeCode Available
Semantically Distributed Robust Optimization for Vision-and-Language Inference	Oct 14, 2021	Data AugmentationNatural Language Inference	CodeCode Available
Semantically Equivalent Adversarial Rules for Debugging NLP models	Jul 1, 2018	Data AugmentationQuestion Answering	CodeCode Available
Adaptive loose optimization for robust question answering	May 6, 2023	Extractive Question-AnsweringMachine Reading Comprehension	CodeCode Available

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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