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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1501–1525 of 2167 papers

Title	Date	Tasks	Status	Hype
VisQA: X-raying Vision and Language Reasoning in Transformers	Apr 2, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
`Just because you are right, doesn't mean I am wrong': Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks	Apr 1, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Towards General Purpose Vision Systems	Apr 1, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training	Apr 1, 2021	Image-text matchingImage-text Retrieval	—Unverified	0
Are Bias Mitigation Techniques for Deep Learning Effective?	Apr 1, 2021	Deep LearningQuestion Answering	CodeCode Available	1
Analysis on Image Set Visual Question Answering	Mar 31, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Domain-robust VQA with diverse datasets and methods but no target labels	Mar 29, 2021	Domain AdaptationObject Recognition	—Unverified	0
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events	Mar 29, 2021	Autonomous VehiclesBenchmarking	CodeCode Available	1
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers	Mar 29, 2021	DecoderImage Segmentation	CodeCode Available	1
'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in the Development and Evaluation of Open-Ended Visual Question Answering (VQA) Tasks	Mar 28, 2021	Question AnsweringVisual Question Answering	CodeCode Available	0
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models	Mar 26, 2021	Question AnsweringVisual Question Answering	—Unverified	0
On the hidden treasure of dialog in video question answering	Mar 26, 2021	Question AnsweringVideo Question Answering	CodeCode Available	1
Visual Grounding Strategies for Text-Only Natural Language Processing	Mar 25, 2021	Image RetrievalLanguage Modeling	—Unverified	0
Multi-Modal Answer Validation for Knowledge-Based VQA	Mar 23, 2021	Question AnsweringRetrieval	CodeCode Available	1
How to Design Sample and Computationally Efficient VQA Models	Mar 22, 2021	Question AnsweringVisual Question Answering	—Unverified	0
A Comprehensive Survey of Scene Graphs: Generation and Application	Mar 17, 2021	Image CaptioningQuestion Answering	—Unverified	0
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA	Mar 17, 2021	Question AnsweringRelational Reasoning	CodeCode Available	0
VMAF And Variants: Towards A Unified VQA	Mar 13, 2021	feature selectionregression	—Unverified	0
Characterizing Misclassifications of Deep NLP Models	Mar 12, 2021	named-entity-recognitionNamed Entity Recognition	—Unverified	0
RL-CSDia: Representation Learning of Computer Science Diagrams	Mar 10, 2021	Question AnsweringRepresentation Learning	—Unverified	0
Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering	Mar 9, 2021	Optical Character Recognition (OCR)Question Answering	CodeCode Available	0
Contextual Dropout: An Efficient Sample-Dependent Dropout Module	Mar 6, 2021	image-classificationImage Classification	CodeCode Available	0
Visual Question Answering: which investigated applications?	Mar 4, 2021	Image CaptioningQuestion Answering	CodeCode Available	0
Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues	Mar 1, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Learning Compositional Representation for Few-shot Visual Question Answering	Feb 21, 2021	AttributeQuestion Answering	—Unverified	0

Show:10 25 50

← PrevPage 61 of 87Next →

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