Key Patch Proposer: Key Patches Contain Rich Information
This work addresses a domain-specific problem in computer vision, offering a potential tool for active learning in semantic segmentation, but it appears incremental as it builds on existing patch-based methods without major breakthroughs.
The paper tackles the problem of selecting key patches in images without extra training, introducing the Key Patch Proposer (KPP) algorithm, which demonstrates robust performance in capturing semantic information through reconstruction and classification tasks.
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.