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 1126–1150 of 2167 papers

Title	Date	Tasks	Status	Hype
FashionVQA: A Domain-Specific Visual Question Answering System	Aug 24, 2022	Question AnsweringVisual Question Answering	—Unverified	0
How good are deep models in understanding the generated images?	Aug 23, 2022	ObjectObject Recognition	—Unverified	0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks	Aug 22, 2022	AllCross-Modal Retrieval	CodeCode Available	0
VLMAE: Vision-Language Masked Autoencoder	Aug 19, 2022	Image-text RetrievalLanguage Modeling	—Unverified	0
Understanding Attention for Vision-and-Language Tasks	Aug 17, 2022	Image GenerationImage Retrieval	CodeCode Available	0
ILLUME: Rationalizing Vision-Language Models through Human Interactions	Aug 17, 2022	Image CaptioningQuestion Answering	CodeCode Available	0
Aesthetic Visual Question Answering of Photographs	Aug 10, 2022	Question AnsweringSentiment Analysis	—Unverified	0
CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning	Aug 10, 2022	MathMathematical Reasoning	CodeCode Available	1
ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding	Aug 5, 2022	Image RetrievalQuestion Answering	CodeCode Available	1
Prompt Tuning for Generative Multimodal Pretrained Models	Aug 4, 2022	Image CaptioningVisual Entailment	—Unverified	0
TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation	Aug 3, 2022	Answer GenerationQuestion-Answer-Generation	CodeCode Available	1
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms	Aug 2, 2022	Neural Architecture SearchVisual Question Answering (VQA)	—Unverified	0
Generative Bias for Robust Visual Question Answering	Aug 1, 2022	Knowledge DistillationQuestion Answering	CodeCode Available	1
Video Question Answering with Iterative Video-Text Co-Tokenization	Aug 1, 2022	Question AnsweringVideo Question Answering	—Unverified	0
Parameter-Parallel Distributed Variational Quantum Algorithm	Jul 31, 2022	Visual Question Answering (VQA)	—Unverified	0
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base	Jul 27, 2022	Question AnsweringSemantic Similarity	—Unverified	0
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection	Jul 26, 2022	DecoderKnowledge Graphs	CodeCode Available	1
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering	Jul 26, 2022	Causal InferenceQuestion Answering	CodeCode Available	1
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models	Jul 25, 2022	Common Sense ReasoningGeneral Knowledge	CodeCode Available	0
Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?	Jul 25, 2022	AllQuestion Answering	—Unverified	0
Towards Complex Document Understanding By Discrete Reasoning	Jul 25, 2022	document understandingQuestion Answering	—Unverified	0
Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem	Jul 24, 2022	DiagnosticQuestion Answering	—Unverified	0
Semantic-aware Modular Capsule Routing for Visual Question Answering	Jul 21, 2022	Question AnsweringVisual Question Answering	—Unverified	0
Rethinking Data Augmentation for Robust Visual Question Answering	Jul 18, 2022	Data AugmentationKnowledge Distillation	CodeCode Available	1
Clover: Towards A Unified Video-Language Alignment and Fusion Model	Jul 16, 2022	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