A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
2020
Neural Information Processing Systems
Zero-shot semantic segmentation (ZSS) aims to classify pixels of novel classes without training examples available. Recently, most ZSS methods focus on learning the visual-semantic correspondence to transfer knowledge from seen classes to unseen classes at the pixel level. Yet, few works study the adverse effects caused by the noisy and outlying training samples of the seen classes. In this paper, we identify this challenge and address it with a novel framework that learns to discriminate noisy
dblp:conf/nips/HuSS20
fatcat:rn222afzmfdo3fntksbyoky2ry