Work

Key Patch Proposer: Key Patches Contain Rich Information

Year: 2024

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2402.11458

Authors: Xu, Jing, Tian, Beiwen, Zhao, Hao

Institutions: Nanjing University of Aeronautics and Astronautics, Tsinghua University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4392011695

Cites: 5

Cited by:

Related to: 10