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 1176–1200 of 2167 papers

Title	Date	Tasks	Status	Hype
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone	Jun 15, 2022	Described Object DetectionImage Captioning	CodeCode Available	1
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning	Jun 13, 2022	Transfer LearningVisual Question Answering (VQA)	CodeCode Available	2
Language Models are General-Purpose Interfaces	Jun 13, 2022	Causal Language ModelingFew-Shot Learning	—Unverified	0
GLIPv2: Unifying Localization and Vision-Language Understanding	Jun 12, 2022	2D Object DetectionContrastive Learning	CodeCode Available	4
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model	Jun 10, 2022	Question AnsweringTask 2	—Unverified	0
cViL: Cross-Lingual Training of Vision-Language Models using Knowledge Distillation	Jun 7, 2022	Knowledge DistillationQuestion Answering	CodeCode Available	0
From Pixels to Objects: Cubic Visual Attention for Visual Question Answering	Jun 4, 2022	ObjectQuestion Answering	—Unverified	0
A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge	Jun 3, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1
Structured Two-stream Attention Network for Video Question Answering	Jun 2, 2022	Question AnsweringVideo Question Answering	—Unverified	0
VL-BEiT: Generative Vision-Language Pretraining	Jun 2, 2022	image-classificationImage Classification	—Unverified	0
REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering	Jun 2, 2022	Question AnsweringRetrieval	CodeCode Available	1
Question Modifiers in Visual Question Answering	Jun 1, 2022	Natural Language UnderstandingQuestion Answering	—Unverified	0
Fine-tuning vs From Scratch: Do Vision & Language Models Have Similar Capabilities on Out-of-Distribution Visual Question Answering?	Jun 1, 2022	Question AnsweringVisual Question Answering	—Unverified	0
Un jeu de données pour répondre à des questions visuelles à propos d’entités nommées en utilisant des bases de connaissances (ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities)	Jun 1, 2022	Question AnsweringVisual Question Answering	—Unverified	0
An Efficient Modern Baseline for FloodNet VQA	May 30, 2022	ManagementVisual Question Answering (VQA)	CodeCode Available	0
Visual Superordinate Abstraction for Robust Concept Learning	May 28, 2022	AttributeQuestion Answering	—Unverified	0
GIT: A Generative Image-to-text Transformer for Vision and Language	May 27, 2022	DecoderImage Captioning	CodeCode Available	2
V-Doc : Visual questions answers with Documents	May 27, 2022	Question AnsweringQuestion Generation	—Unverified	0
Avoiding Barren Plateaus with Classical Deep Neural Networks	May 26, 2022	Visual Question Answering (VQA)	—Unverified	0
Guiding Visual Question Answering with Attention Priors	May 25, 2022	Question AnsweringVisual Grounding	—Unverified	0
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections	May 24, 2022	Computational Efficiencycross-modal alignment	CodeCode Available	1
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization	May 24, 2022	Image CaptioningOut-of-Distribution Generalization	—Unverified	0
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization	May 24, 2022	DescriptiveImage Captioning	—Unverified	0
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering	May 23, 2022	Knowledge GraphsQuestion Answering	—Unverified	0
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models	May 23, 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