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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1031–1040 of 2167 papers

Title	Date	Tasks	Status	Hype
ParsVQA-Caps: A Benchmark for Visual Question Answering and Image Captioning in Persian	Dec 7, 2022	Image CaptioningQuestion Answering	—Unverified	0
Review of Ansatz Designing Techniques for Variational Quantum Algorithms	Dec 7, 2022	Visual Question Answering (VQA)	—Unverified	0
Hierarchical multimodal transformers for Multi-Page DocVQA	Dec 7, 2022	DecoderQuestion Answering	CodeCode Available	1
InternVideo: General Video Foundation Models via Generative and Discriminative Learning	Dec 6, 2022	Action ClassificationAction Recognition	CodeCode Available	4
Unifying Vision, Text, and Layout for Universal Document Processing	Dec 5, 2022	Document AIdocument understanding	CodeCode Available	3
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests	Dec 3, 2022	Question AnsweringVisual Question Answering	CodeCode Available	0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning	Dec 2, 2022	DecoderLanguage Modeling	—Unverified	0
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning	Dec 1, 2022	Domain GeneralizationQuestion Answering	CodeCode Available	1
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks	Nov 30, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Optimizing Explanations by Network Canonization and Hyperparameter Search	Nov 30, 2022	Explainable Artificial Intelligence (XAI)image-classification	—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