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 1171–1180 of 2167 papers

Title	Date	Tasks	Status
Advancing Video Quality Assessment for AIGC	Sep 23, 2024	Image GenerationText Generation	—Unverified
AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?	Dec 4, 2024	BenchmarkingVisual Question Answering (VQA)	—Unverified
Adventurer's Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering based on Scene Graphs	Jun 28, 2021	Question AnsweringTask 2	—Unverified
Adversarial Attacks Beyond the Image Space	Nov 20, 2017	Question AnsweringVisual Question Answering	—Unverified
Adversarial Multimodal Network for Movie Question Answering	Jun 24, 2019	Question AnsweringVideo Question Answering	—Unverified
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects	Jun 20, 2019	Question AnsweringVisual Question Answering	—Unverified
Adversarial Representation Learning for Text-to-Image Matching	Aug 28, 2019	Image CaptioningLanguage Modeling	—Unverified
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models	Jun 1, 2021	Data AugmentationQuestion Answering	—Unverified
Aesthetic Visual Question Answering of Photographs	Aug 10, 2022	Question AnsweringSentiment Analysis	—Unverified
A Focused Dynamic Attention Model for Visual Question Answering	Apr 6, 2016	Question AnsweringVisual Question 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	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