Summary of Task Consistent Prototype Learning For Incremental Few-shot Semantic Segmentation, by Wenbo Xu et al.
Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation
by Wenbo Xu, Yanan Wu, Haoran Jiang, Yang Wang, Qiang Wu, Jian Zhang
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Incremental Few-Shot Semantic Segmentation (iFSS) model tackles a challenging task that requires continuous expansion of its segmentation capabilities on novel classes using only a few annotated examples. The paper addresses the issue of misaligned objectives between base training and incremental learning phases, which can lead to suboptimal performance. To overcome this challenge, the authors introduce a meta-learning-based approach that simulates incremental evaluation during base training, enabling rapid adaptation without forgetting. Additionally, they propose prototype space redistribution learning to dynamically update class prototypes for optimal inter-prototype boundaries. The results on iFSS datasets built upon PASCAL and COCO benchmarks demonstrate the superiority of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new model that can quickly learn about new things by using just a few examples. This is helpful because it’s hard to train models to do this task, especially when there are many classes to learn. The authors come up with a way to make their model better by training it on pretend tasks during the learning process. They also find a way to help the model understand how to tell apart different things. This makes the model really good at learning new things without forgetting what it already knows. |
Keywords
» Artificial intelligence » Few shot » Meta learning » Semantic segmentation
