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 526–550 of 2167 papers

Title	Date	Tasks	Status	Hype
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering	Sep 18, 2020	Out-of-Distribution GeneralizationQuestion Answering	CodeCode Available	1
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports	Sep 3, 2020	Image-text RetrievalMedical Visual Question Answering	CodeCode Available	1
A Dataset and Baselines for Visual Question Answering on Art	Aug 28, 2020	Question AnsweringQuestion Generation	CodeCode Available	1
DeVLBert: Learning Deconfounded Visio-Linguistic Representations	Aug 16, 2020	Image RetrievalQuestion Answering	CodeCode Available	1
Spatially Aware Multimodal Transformers for TextVQA	Jul 23, 2020	Optical Character Recognition (OCR)Spatial Reasoning	CodeCode Available	1
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering	Jul 19, 2020	Adversarial AttackData Augmentation	CodeCode Available	1
Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions	Jul 17, 2020	Question AnsweringVideo Question Answering	CodeCode Available	1
Learning to Discretely Compose Reasoning Module Networks for Video Captioning	Jul 17, 2020	DecoderQuestion Answering	CodeCode Available	1
DocVQA: A Dataset for VQA on Document Images	Jul 1, 2020	Question AnsweringReading Comprehension	CodeCode Available	1
Visual Question Generation from Radiology Images	Jul 1, 2020	Image AugmentationQuestion Generation	CodeCode Available	1
Ontology-guided Semantic Composition for Zero-Shot Learning	Jun 30, 2020	image-classificationImage Classification	CodeCode Available	1
Graph Optimal Transport for Cross-Domain Alignment	Jun 26, 2020	Graph MatchingImage Captioning	CodeCode Available	1
Sparse and Continuous Attention Mechanisms	Jun 12, 2020	Machine TranslationQuestion Answering	CodeCode Available	1
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning	Jun 11, 2020	Question AnsweringReinforcement Learning (RL)	CodeCode Available	1
Large-Scale Adversarial Training for Vision-and-Language Representation Learning	Jun 11, 2020	Image-text RetrievalQuestion Answering	CodeCode Available	1
Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?	Jun 9, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1
Counterfactual VQA: A Cause-Effect Look at Language Bias	Jun 8, 2020	Causal Inferencecounterfactual	CodeCode Available	1
Attention-Based Context Aware Reasoning for Situation Recognition	Jun 1, 2020	Action RecognitionFine-grained Action Recognition	CodeCode Available	1
Structured Multimodal Attentions for TextVQA	Jun 1, 2020	Graph AttentionOptical Character Recognition (OCR)	CodeCode Available	1
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content	May 29, 2020	Benchmarkingfeature selection	CodeCode Available	1
Cross-Modality Relevance for Reasoning on Language and Vision	May 12, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1
COBRA: Contrastive Bi-Modal Representation Algorithm	May 7, 2020	Cross-Modal RetrievalImage Captioning	CodeCode Available	1
Dynamic Language Binding in Relational Visual Reasoning	Apr 30, 2020	ObjectQuestion Answering	CodeCode Available	1
Deep Multimodal Neural Architecture Search	Apr 25, 2020	DecoderImage-text matching	CodeCode Available	1
Visual Grounding Methods for VQA are Working for the Wrong Reasons!	Apr 12, 2020	Question AnsweringVisual Grounding	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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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