Key Patch Proposer: Key Patches Contain Rich Information
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 18 Feb 2024.
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References · 7
- 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
- 2019Variational Adversarial Active Learningcited by 284
- 2019Parting with Illusions about Deep Active Learningcited by 38
- 2021All you need are a few pixels: semantic segmentation with PixelPickcited by 30
- 2020Importance of Self-Consistency in Active Learning for Semantic Segmentationcited by 21
- 2023Best Practices in Active Learning for Semantic Segmentationcited by 10
- 2020Very Fast Streaming Submodular Function Maximizationcited by 6
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