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 341–350 of 2167 papers

Title	Date	Tasks	Status	Hype
Multimodal Federated Learning via Contrastive Representation Ensemble	Feb 17, 2023	Federated LearningImage-text Retrieval	CodeCode Available	1
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling	Feb 13, 2023	Image-text RetrievalRetrieval	CodeCode Available	1
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications	Feb 1, 2023	Question AnsweringRepresentation Learning	CodeCode Available	1
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers	Jan 31, 2023	Image CaptioningImage Classification	CodeCode Available	1
SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images	Jan 12, 2023	Evidence SelectionQuestion Answering	CodeCode Available	1
Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering	Jan 11, 2023	Question AnsweringReading Comprehension	CodeCode Available	1
VQACL: A Novel Visual Question Answering Continual Learning Setting	Jan 1, 2023	Continual LearningQuestion Answering	CodeCode Available	1
Variational Causal Inference Network for Explanatory Visual Question Answering	Jan 1, 2023	Explanation GenerationExplanatory Visual Question Answering	CodeCode Available	1
MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering	Dec 19, 2022	FormQuestion Answering	CodeCode Available	1
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations	Dec 8, 2022	Explanation GenerationVisual Entailment	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