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 1426–1450 of 2167 papers

Title	Date	Tasks	Status
Attention Mechanism based Cognition-level Scene Understanding	Apr 17, 2022	Question AnsweringScene Understanding	—Unverified
Improving Cross-Modal Understanding in Visual Dialog via Contrastive Learning	Apr 15, 2022	Contrastive LearningQuestion Answering	—Unverified
Question-Driven Graph Fusion Network For Visual Question Answering	Apr 3, 2022	Graph AttentionObject	—Unverified
Co-VQA : Answering by Interactive Sub Question Sequence	Apr 2, 2022	Question AnsweringVisual Question Answering	—Unverified
Perceptual Quality Assessment of UGC Gaming Videos	Mar 31, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
SimVQA: Exploring Simulated Environments for Visual Question Answering	Mar 31, 2022	Data AugmentationDiversity	—Unverified
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers	Mar 30, 2022	Question AnsweringVisual Commonsense Reasoning	CodeCode Available
Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment	Mar 28, 2022	Image Quality AssessmentVideo Quality Assessment	—Unverified
Single-Stream Multi-Level Alignment for Vision-Language Pretraining	Mar 27, 2022	Image-text RetrievalQuestion Answering	CodeCode Available
Subjective and Objective Analysis of Streamed Gaming Videos	Mar 24, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering	Mar 24, 2022	GPUQuestion Answering	CodeCode Available
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering	Mar 24, 2022	Optical Character RecognitionOptical Character Recognition (OCR)	—Unverified
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models	Mar 22, 2022	Image CaptioningImage Generation	—Unverified
Can you even tell left from right? Presenting a new challenge for VQA	Mar 15, 2022	Question AnsweringVisual Question Answering	—Unverified
CARETS: A Consistency And Robustness Evaluative Test Suite for VQA	Mar 15, 2022	NegationQuestion Generation	CodeCode Available
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment	Mar 14, 2022	parameter-efficient fine-tuningQuestion Answering	—Unverified
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation	Mar 12, 2022	Image CaptioningKnowledge Distillation	—Unverified
Barlow constrained optimization for Visual Question Answering	Mar 7, 2022	Question AnsweringVisual Question Answering	CodeCode Available
Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question Answering	Mar 6, 2022	Graph AttentionQuestion Answering	CodeCode Available
Modeling Coreference Relations in Visual Dialog	Mar 6, 2022	Question AnsweringVisual Dialog	—Unverified
Recent, rapid advancement in visual question answering architecture: a review	Mar 2, 2022	Question AnsweringVisual Question Answering	—Unverified
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment	Mar 1, 2022	RetrievalSentence	—Unverified
Joint Answering and Explanation for Visual Commonsense Reasoning	Feb 25, 2022	Knowledge DistillationQuestion Answering	CodeCode Available
On Modality Bias Recognition and Reduction	Feb 25, 2022	Action RecognitionMulti-modal Classification	CodeCode Available
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models	Feb 24, 2022	BenchmarkingDiagnostic	—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