Summary of S3pt: Scene Semantics and Structure Guided Clustering to Boost Self-supervised Pre-training For Autonomous Driving, by Maciej K. Wozniak et al.
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving
by Maciej K. Wozniak, Hariprasath Govindarajan, Marvin Klingner, Camille Maurice, B Ravi Kiran, Senthil Yogamani
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 Recent self-supervised clustering-based pre-training techniques have achieved impressive results for downstream detection and segmentation tasks. However, real-world applications such as autonomous driving face challenges with imbalanced object class and size distributions and complex scene geometries. The proposed S3PT method addresses these issues by incorporating semantic distribution consistent clustering to handle rare classes, object diversity consistent spatial clustering to handle diverse object sizes, and depth-guided spatial clustering to regularize learning based on geometric information of the scene. These contributions lead to significant improvements in performance for downstream tasks such as semantic segmentation and 3D object detection on nuScenes, nuImages, and Cityscapes datasets, with promising domain translation properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to train computers to recognize objects and scenes more accurately. The problem is that current methods don’t handle rare or small objects well, especially in complex environments like roads. To fix this, the authors suggest using three strategies: 1) make sure representations capture rare classes like animals or motorcycles; 2) group similar-sized objects together; and 3) use depth information to separate objects in a scene. By doing so, the method improves performance on tasks like recognizing objects and scenes, with potential for real-world applications like autonomous driving. |
Keywords
» Artificial intelligence » Clustering » Object detection » Self supervised » Semantic segmentation » Translation
