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 1221–1230 of 2167 papers

Title	Date	Tasks	Status
Transferring Visual Attributes from Natural Language to Verified Image Generation	May 24, 2023	Image GenerationText to Image Generation	—Unverified
Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach	May 23, 2023	Image ManipulationQuestion Answering	—Unverified
DUBLIN -- Document Understanding By Language-Image Network	May 23, 2023	Document Classificationdocument understanding	—Unverified
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design	May 22, 2023	image-classificationImage Classification	—Unverified
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending	May 22, 2023	Question AnsweringRetrieval	—Unverified
Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature	May 18, 2023	Question AnsweringVisual Question Answering	—Unverified
An Empirical Study on the Language Modal in Visual Question Answering	May 17, 2023	Question AnsweringVisual Question Answering	—Unverified
TG-VQA: Ternary Game of Video Question Answering	May 17, 2023	Contrastive LearningQuestion Answering	—Unverified
A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning	May 17, 2023	Quantum Machine LearningVisual Question Answering (VQA)	—Unverified
SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement	May 15, 2023	Video EnhancementVideo Quality Assessment	—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