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 1101–1110 of 2167 papers

Title	Date	Tasks	Status
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization	Nov 1, 2023	Domain GeneralizationQuestion Answering	—Unverified
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering	May 23, 2022	Knowledge GraphsQuestion Answering	—Unverified
VQA-LOL: Visual Question Answering under the Lens of Logic	Feb 19, 2020	NegationQuestion Answering	—Unverified
VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering	Sep 27, 2021	Question AnsweringVisual Question Answering	—Unverified
VQA Training Sets are Self-play Environments for Generating Few-shot Pools	May 30, 2024	Question AnsweringVisual Question Answering	—Unverified
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models	Feb 16, 2024	Adversarial RobustnessLanguage Modelling	—Unverified
VQA with Cascade of Self- and Co-Attention Blocks	Feb 28, 2023	Question AnsweringVisual Question Answering	—Unverified
VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images	May 6, 2024	AttributeLanguage Modeling	—Unverified
Watching the News: Towards VideoQA Models that can Read	Nov 10, 2022	Question AnsweringVideo Question Answering	—Unverified
Weakly Supervised Visual Question Answer Generation	Jun 11, 2023	Answer GenerationDependency Parsing	—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