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 1651–1675 of 2167 papers

Title	Date	Tasks	Status
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding	Dec 29, 2020	Document Image ClassificationDocument Layout Analysis	CodeCode Available
Object-Centric Diagnosis of Visual Reasoning	Dec 21, 2020	DiagnosticObject	—Unverified
Learning content and context with language bias for Visual Question Answering	Dec 21, 2020	Question AnsweringVisual Question Answering	CodeCode Available
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA	Dec 20, 2020	Visual Question Answering (VQA)	—Unverified
On Modality Bias in the TVQA Dataset	Dec 18, 2020	Question AnsweringVideo Question Answering	CodeCode Available
Trying Bilinear Pooling in Video-QA	Dec 18, 2020	Question AnsweringVideo Question Answering	—Unverified
KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning	Dec 13, 2020	SentenceVisual Commonsense Reasoning	—Unverified
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps	Dec 9, 2020	DecoderImage Captioning	—Unverified
Study on the Assessment of the Quality of Experience of Streaming Video	Dec 8, 2020	regressionVideo Quality Assessment	CodeCode Available
Understanding Guided Image Captioning Performance across Domains	Dec 4, 2020	DescriptiveImage Captioning	CodeCode Available
WeaQA: Weak Supervision via Captions for Visual Question Answering	Dec 4, 2020	Question AnsweringVisual Question Answering	—Unverified
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering	Dec 1, 2020	Graph Neural NetworkQuestion Answering	—Unverified
Open-Ended Multi-Modal Relational Reasoning for Video Question Answering	Dec 1, 2020	Question AnsweringRelational Reasoning	CodeCode Available
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering	Dec 1, 2020	Question AnsweringVisual Question Answering	—Unverified
Towards Knowledge-Augmented Visual Question Answering	Dec 1, 2020	General KnowledgeGraph Attention	CodeCode Available
Learning from Lexical Perturbations for Consistent Visual Question Answering	Nov 26, 2020	Question AnsweringVisual Question Answering	CodeCode Available
Siamese Tracking with Lingual Object Constraints	Nov 23, 2020	ObjectObject Tracking	CodeCode Available
Interpretable Visual Reasoning via Induced Symbolic Space	Nov 23, 2020	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available
Modular Graph Attention Network for Complex Visual Relational Reasoning	Nov 22, 2020	Graph AttentionQuestion Answering	—Unverified
Logically Consistent Loss for Visual Question Answering	Nov 19, 2020	Multi-Task LearningQuestion Answering	—Unverified
Generating Natural Questions from Images for Multimodal Assistants	Nov 17, 2020	Common Sense ReasoningNatural Questions	—Unverified
CapWAP: Captioning with a Purpose	Nov 9, 2020	Image CaptioningQuestion Answering	—Unverified
Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles	Nov 7, 2020	Natural Language InferenceQuestion Answering	CodeCode Available
An Improved Attention for Visual Question Answering	Nov 4, 2020	DecoderQuestion Answering	CodeCode Available
Reasoning Over History: Context Aware Visual Dialog	Nov 2, 2020	coreference-resolutionCoreference Resolution	—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