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 476–500 of 2167 papers

Title	Date	Tasks	Status	Hype
NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions	May 18, 2021	Question AnsweringVideo Question Answering	CodeCode Available	1
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules	May 11, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning	May 10, 2021	Arithmetic ReasoningGeometry Problem Solving	CodeCode Available	1
Passage Retrieval for Outside-Knowledge Visual Question Answering	May 9, 2021	Image CaptioningObject	CodeCode Available	1
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding	Apr 26, 2021	Generalized Referring Expression ComprehensionPhrase Grounding	CodeCode Available	1
RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition	Apr 24, 2021	Image CaptioningObject Recognition	CodeCode Available	1
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering	Apr 20, 2021	Graph Neural NetworkGraph Question Answering	CodeCode Available	1
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering	Apr 7, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
MMBERT: Multimodal BERT Pretraining for Improved Medical VQA	Apr 3, 2021	Language ModelingLanguage Modelling	CodeCode Available	1
VisQA: X-raying Vision and Language Reasoning in Transformers	Apr 2, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
Towards General Purpose Vision Systems	Apr 1, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
Are Bias Mitigation Techniques for Deep Learning Effective?	Apr 1, 2021	Deep LearningQuestion Answering	CodeCode Available	1
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers	Mar 29, 2021	DecoderImage Segmentation	CodeCode Available	1
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events	Mar 29, 2021	Autonomous VehiclesBenchmarking	CodeCode Available	1
On the hidden treasure of dialog in video question answering	Mar 26, 2021	Question AnsweringVideo Question Answering	CodeCode Available	1
Multi-Modal Answer Validation for Knowledge-Based VQA	Mar 23, 2021	Question AnsweringRetrieval	CodeCode Available	1
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer	Feb 18, 2021	DecoderDocument Image Classification	CodeCode Available	1
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering	Feb 18, 2021	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts	Feb 17, 2021	Caption GenerationDiversity	CodeCode Available	1
Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling	Feb 11, 2021	Question AnsweringRetrieval	CodeCode Available	1
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision	Feb 5, 2021	Cross-Modal RetrievalImage Retrieval	CodeCode Available	1
Unifying Vision-and-Language Tasks via Text Generation	Feb 4, 2021	Conditional Text GenerationDecoder	CodeCode Available	1
VisualMRC: Machine Reading Comprehension on Document Images	Jan 27, 2021	Machine Reading ComprehensionNatural Language Understanding	CodeCode Available	1
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images	Jan 1, 2021	AttributeMultiple Instance Learning	CodeCode Available	1
TRAR: Routing the Attention Spans in Transformer for Visual Question Answering	Jan 1, 2021	Question AnsweringReferring Expression	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