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 1571–1580 of 2167 papers

Title	Date	Tasks	Status
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training	Jun 25, 2021	Image-text RetrievalQuestion Answering	—Unverified
A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 2021	Jun 24, 2021	Visual Question Answering (VQA)	—Unverified
FOVQA: Blind Foveated Video Quality Assessment	Jun 24, 2021	Video CompressionVideo Quality Assessment	—Unverified
VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis	Jun 19, 2021	Question AnsweringVisual Question Answering	—Unverified
How Modular Should Neural Module Networks Be for Systematic Generalization?	Jun 15, 2021	Question AnsweringSystematic Generalization	CodeCode Available
Assessment of Subjective and Objective Quality of Live Streaming Sports Videos	Jun 15, 2021	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
NAAQA: A Neural Architecture for Acoustic Question Answering	Jun 11, 2021	Acoustic Question AnsweringQuestion Answering	CodeCode Available
Supervising the Transfer of Reasoning Patterns in VQA	Jun 10, 2021	PAC learningTransfer Learning	—Unverified
Bayesian Attention Belief Networks	Jun 9, 2021	DecoderMachine Translation	—Unverified
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions	Jun 8, 2021	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	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