Summary of Pcotta: Continual Test-time Adaptation For Multi-task Point Cloud Understanding, by Jincen Jiang et al.
PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding
by Jincen Jiang, Qianyu Zhou, Yuhang Li, Xinkui Zhao, Meili Wang, Lizhuang Ma, Jian Chang, Jian Jun Zhang, Xuequan Lu
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| 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 paper presents PCoTTA, a pioneering framework for Continual Test-Time Adaptation (CoTTA) in multi-task point cloud understanding. The framework enhances the model’s transferability to the continually changing target domain by introducing a multi-task setting and three key components: APM, GSFS, and CPR. APM balances source and learnable prototypes to avoid catastrophic forgetting, while GSFS shifts testing samples online to mitigate error accumulation. CPR pulls nearest prototypes close and pushes them away from others, making each prototype distinguishable during adaptation. The framework is demonstrated through experimental comparisons, leading to a new benchmark that showcases its superiority in boosting the model’s transferability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PCoTTA is a special way for machines to learn about point clouds. It helps the machine understand point clouds better as they change over time. This is important because it means the machine can use what it learned before to improve its understanding of new, changing point clouds. The PCoTTA framework has three main parts: APM, GSFS, and CPR. These parts work together to help the machine learn from new data without forgetting what it already knew. |
Keywords
» Artificial intelligence » Boosting » Multi task » Transferability
