ADeLA: Automatic Dense Labeling with Attention for Viewpoint Shift in Semantic Segmentation

We describe a method to deal with performance drop in semantic segmentation caused by viewpoint changes within multi-camera systems, where temporally paired images are readily available, but the annotations may only be abundant for a few typical views. Existing methods alleviate performance drop via...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 8069 - 8079
Hlavní autori:	Ren, Hanxiang, Yang, Yanchao, Wang, He, Shen, Bokui, Fan, Qingnan, Zheng, Youyi, Liu, C. Karen, Guibas, Leonidas
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2022
Predmet:	Color grouping and shape analysis; Vision applications and systems Image segmentation Object segmentation Robot vision systems Semantics Shape Transfer/low-shot/long-tail learning; Datasets and evaluation; Image and video synthesis and generation; Machine learning; Robot vision; Scene analysis and understanding; Segmentation Uncertainty
ISSN:	1063-6919
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