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 976–1000 of 2167 papers

Title	Date	Tasks	Status	Hype
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax	Mar 2, 2023	DescriptiveImage Captioning	CodeCode Available	1
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs	Mar 2, 2023	ArticlesMedical Visual Question Answering	CodeCode Available	1
MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering	Mar 2, 2023	Mixture-of-ExpertsQuestion Answering	CodeCode Available	1
RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training	Mar 1, 2023	Question AnsweringRetrieval	CodeCode Available	1
VQA with Cascade of Self- and Co-Attention Blocks	Feb 28, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion	Feb 26, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
Medical visual question answering using joint self-supervised learning	Feb 25, 2023	DecoderDiversity	—Unverified	0
EVJVQA Challenge: Multilingual Visual Question Answering	Feb 23, 2023	Language ModelingLanguage Modelling	—Unverified	0
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?	Feb 23, 2023	Open-Domain Question AnsweringQuestion Answering	CodeCode Available	1
VinVL+L: Enriching Visual Representation with Location Context in VQA	Feb 22, 2023	Question AnsweringTAG	CodeCode Available	0
Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities	Feb 22, 2023	Entity LinkingFine-Grained Image Recognition	CodeCode Available	1
Few-shot Multimodal Multitask Multilingual Learning	Feb 19, 2023	Few-Shot LearningIn-Context Learning	—Unverified	0
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning	Feb 19, 2023	Graph LearningMedical Visual Question Answering	—Unverified	0
Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering	Feb 18, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Multimodal Federated Learning via Contrastive Representation Ensemble	Feb 17, 2023	Federated LearningImage-text Retrieval	CodeCode Available	1
Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts	Feb 17, 2023	Image RetrievalImage-text Classification	CodeCode Available	1
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling	Feb 13, 2023	Image-text RetrievalRetrieval	CodeCode Available	1
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis	Feb 11, 2023	Image-text RetrievalKnowledge Graphs	CodeCode Available	0
Is Multimodal Vision Supervision Beneficial to Language?	Feb 10, 2023	Image RetrievalNatural Language Understanding	CodeCode Available	0
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications	Feb 1, 2023	Question AnsweringRepresentation Learning	CodeCode Available	1
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video	Feb 1, 2023	Action ClassificationImage Classification	CodeCode Available	4
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers	Jan 31, 2023	Image CaptioningImage Classification	CodeCode Available	1
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models	Jan 30, 2023	Generative Visual Question AnsweringImage Captioning	CodeCode Available	4
BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA Models	Jan 28, 2023	Out-of-Distribution GeneralizationQuestion Answering	CodeCode Available	0
Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering	Jan 25, 2023	DecoderExplanation Generation	—Unverified	0

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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