Summary of Lab-cl: Localized and Balanced Contrastive Learning For Improving Parking Slot Detection, by U Jin Jeong et al.
LaB-CL: Localized and Balanced Contrastive Learning for improving parking slot detection
by U Jin Jeong, Sumin Roh, Il Yong Chun
First submitted to arxiv on: 10 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 In this paper, researchers develop a novel approach to improve parking slot detection in autonomous systems. The problem is divided into two classification tasks: identifying whether localized candidates are junctions of parking slots or not, and determining the shape of detected junctions. To overcome the data imbalance issue common in these problems, the authors propose a supervised contrastive learning framework called LaB-CL. This approach includes class prototypes to consider representations from all classes and a new hard negative sampling scheme that selects local representations with high prediction error. Experimental results on a benchmark dataset show that LaB-CL outperforms existing parking slot detection methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Parking slot detection is important for autonomous parking systems. The problem involves two tasks: identifying whether candidates are junctions of parking slots or not, and determining the shape of detected junctions. Sometimes, one type of candidate might be more common than others, making it harder to detect the less common ones. To fix this issue, researchers created a new way to learn called LaB-CL (Localized and Balanced Contrastive Learning). It includes two main ideas: using class prototypes and selecting hard negative samples. This approach is tested on a benchmark dataset and shows better results than other methods. |
Keywords
» Artificial intelligence » Classification » Supervised
