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 331–340 of 2167 papers

Title	Date	Tasks	Status	Hype
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models	Mar 10, 2023	Language ModelingLanguage Modelling	CodeCode Available	1
Prismer: A Vision-Language Model with Multi-Task Experts	Mar 4, 2023	Few-Shot LearningImage Captioning	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
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
RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training	Mar 1, 2023	Question AnsweringRetrieval	CodeCode Available	1
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion	Feb 26, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?	Feb 23, 2023	Open-Domain Question AnsweringQuestion Answering	CodeCode Available	1
Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities	Feb 22, 2023	Entity LinkingFine-Grained Image Recognition	CodeCode Available	1
Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts	Feb 17, 2023	Image RetrievalImage-text Classification	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