Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-trained vision-language models, e.g., CLIP, t...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 7061 - 7070
Hlavní autori:	Liang, Feng, Wu, Bichen, Dai, Xiaoliang, Li, Kunpeng, Zhao, Yinan, Zhang, Hang, Zhang, Peizhao, Vajda, Peter, Marculescu, Diana
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2023
Predmet:	Adaptation models and reasoning Computational modeling language Proposals Semantic segmentation Semantics Training Training data Vision
ISSN:	1063-6919
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