# Key Patch Proposer: Key Patches Contain Rich Information

arXiv: https://arxiv.org/abs/2402.11458 · HTML: https://mlsanity.com/paper/2402.11458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [Parting with Illusions about Deep Active Learning](https://mlsanity.com/paper/1912.05361/) (2019)
- [All you need are a few pixels: semantic segmentation with PixelPick](https://mlsanity.com/paper/2104.06394/) (2021)
- [Importance of Self-Consistency in Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2008.01860/) (2020)
- [Best Practices in Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2302.04075/) (2023)
- [Very Fast Streaming Submodular Function Maximization](https://mlsanity.com/paper/2010.10059/) (2020)

## 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.