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 1231–1240 of 2167 papers

Title	Date	Tasks	Status
OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese	May 7, 2023	Information RetrievalQuestion Answering	CodeCode Available
Adaptive loose optimization for robust question answering	May 6, 2023	Extractive Question-AnsweringMachine Reading Comprehension	CodeCode Available
Analysis of Visual Question Answering Algorithms with attention model	May 4, 2023	Question AnsweringVisual Question Answering	—Unverified
GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content	May 3, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients	Apr 27, 2023	Quantum Machine LearningVisual Question Answering (VQA)	—Unverified
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos	Apr 25, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Making Video Quality Assessment Models Robust to Bit Depth	Apr 25, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
PDFVQA: A New Dataset for Real-World VQA on PDF Documents	Apr 13, 2023	document understandingKey Information Extraction	—Unverified
CAVL: Learning Contrastive and Adaptive Representations of Vision and Language	Apr 10, 2023	Image RetrievalPhrase Grounding	—Unverified
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions	Apr 6, 2023	In-Context LearningQuestion Answering	—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