Key Patch Proposer: Key Patches Contain Rich Information
Jing Xu Beiwen Tian Hao Zhao
In this paper, we introduce a novel algorithm named Key Patch Proposer (KPP)
designed to select key patches in an image without additional training. Our
experiments showcase KPP's robust capacity to capture semantic information by
both reconstruction and classification tasks. The efficacy of KPP suggests its
potential application in active learning for semantic segmentation. Our source
code is publicly available at https://github.com/CA-TT-AC/key-patch-proposer.