A Few Guidelines for Incremental Few-Shot Segmentation

By Fabio Cermelli, Massimiliano Mancini, Yongqin Xian, Zeynep Akata, Barbara Caputo. 2020

10 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Arthur Douillard
2. Fabio Cermelli
3. Matthieu Cord
4. Yifu Chen
5. Arnaud Dapogny
6. Da-Wei Zhou
7. Dario Fontanel
8. Barbara Caputo

Citations in the corpus

• Incremental Learning in Semantic Segmentation from Image Labels (2021, background): “Finally, [9] proposed the incremental few-shot segmentation setting, where only a few images to learn new classes are provided.”
• Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation (2021, methodology): “both the proposed PLOP and PLOPLong outperform stateof-the-art approaches, MiB [15], SDR [76], and GIFS [75] on all evaluated settings by a significant margin.”
• Class-Incremental Few-Shot Object Detection (2021, methodology): “HM(x, y) = 2xy/(x+y), same as another incremental fewshot scenario [Cermelli et al., 2020].”