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 1491–1500 of 2167 papers

Title	Date	Tasks	Status	Hype
VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks	Apr 16, 2021	Information RetrievalQuestion Answering	—Unverified	0
Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention	Apr 14, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation	Apr 13, 2021	Knowledge DistillationTriplet	—Unverified	0
CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over Images	Apr 13, 2021	Question AnsweringVisual Question Answering	CodeCode Available	0
Neuro-Symbolic VQA: A review from the perspective of AGI desiderata	Apr 13, 2021	Question AnsweringVisual Question Answering	—Unverified	0
How Transferable are Reasoning Patterns in VQA?	Apr 8, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Multimodal Continuous Visual Attention Mechanisms	Apr 7, 2021	ClusteringQuestion Answering	—Unverified	0
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering	Apr 7, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
Compressing Visual-linguistic Model via Knowledge Distillation	Apr 5, 2021	Image CaptioningKnowledge Distillation	—Unverified	0
MMBERT: Multimodal BERT Pretraining for Improved Medical VQA	Apr 3, 2021	Language ModelingLanguage Modelling	CodeCode Available	1

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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