Summary of Mos: Model Synergy For Test-time Adaptation on Lidar-based 3d Object Detection, by Zhuoxiao Chen et al.
MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection
by Zhuoxiao Chen, Junjie Meng, Mahsa Baktashmotlagh, Yonggang Zhang, Zi Huang, Yadan Luo
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 A novel online test-time adaptation framework for 3D object detection is proposed to address performance degradation in real-world deployments due to domain shifts. The framework, called Model Synergy (MOS), leverages long-term knowledge from previous test batches to mitigate catastrophic forgetting and adapt to diverse shifts. MOS includes a synergy weights (SW) strategy that dynamically selects historical checkpoints with diverse knowledge and assembles them to best accommodate the current test batch. This approach is tested against existing test-time adaptation strategies across three datasets and eight types of corruptions, demonstrating superior adaptability to dynamic scenes and conditions. Notably, it achieves a 67.3% improvement in a challenging cross-corruption scenario. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how computers detect objects in 3D images using LiDAR technology. Right now, this kind of detection can get worse when the environment or weather changes. The researchers created a new way to help computers adapt to these changes by looking at what they learned from earlier experiences. This approach is called Model Synergy and it helps the computer make better predictions even in tough situations. |
Keywords
» Artificial intelligence » Object detection
