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 651–660 of 2167 papers

Title	Date	Tasks	Status	Score
Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances	Sep 18, 2022	AttributeQuestion Answering	CodeCode Available	5
Declarative Knowledge Distillation from Large Language Models for Visual Question Answering Datasets	Oct 12, 2024	Knowledge DistillationQuestion Answering	CodeCode Available	5
Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks	Jul 30, 2024	Visual Question Answering (VQA)	CodeCode Available	5
An Efficient Modern Baseline for FloodNet VQA	May 30, 2022	ManagementVisual Question Answering (VQA)	CodeCode Available	5
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering	Dec 19, 2024	Contrastive LearningLanguage Modeling	CodeCode Available	5
A Diagram Is Worth A Dozen Images	Mar 24, 2016	Visual Question Answering (VQA)	CodeCode Available	5
Multimodal Residual Learning for Visual QA	Jun 5, 2016	Multiple-choiceQuestion Answering	CodeCode Available	5
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering	Aug 4, 2017	Question AnsweringVisual Question Answering	CodeCode Available	5
Barlow constrained optimization for Visual Question Answering	Mar 7, 2022	Question AnsweringVisual Question Answering	CodeCode Available	5
Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question Answering	May 21, 2024	DiversityInformation Retrieval	CodeCode Available	5

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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