Summary of Sckd: Semi-supervised Cross-modality Knowledge Distillation For 4d Radar Object Detection, by Ruoyu Xu et al.
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection
by Ruoyu Xu, Zhiyu Xiang, Chenwei Zhang, Hanzhi Zhong, Xijun Zhao, Ruina Dang, Peng Xu, Tianyu Pu, Eryun Liu
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed Semi-supervised Cross-modality Knowledge Distillation (SCKD) method for 4D radar-based 3D object detection is a novel approach that leverages the strengths of both Lidar and radar sensors to improve the performance of autonomous vehicles. By characterizing the feature learning capabilities of a Lidar-radar-fused teacher network through semi-supervised distillation, SCKD enables the transfer of knowledge across modalities, leading to improved object detection accuracy. The adaptive fusion module in the teacher network boosts its performance, while two feature distillation modules facilitate cross-modality knowledge transfer. A semi-supervised output distillation module further increases the effectiveness and flexibility of the framework. Experimental results demonstrate significant improvements over state-of-the-art works on both VoD and ZJUODset datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to help cars see objects around them, using a combination of two sensors: Lidar (like a laser scanner) and radar (which uses radio waves). These sensors are good at detecting different things, like obstacles or other cars. But the radar sensor is particularly useful in bad weather conditions, as it can still work well even when visibility is poor. However, the data from the radar sensor is noisy and sparse, making it harder to detect objects accurately. The proposed method, called SCKD, uses a special type of learning called distillation to help the radar sensor learn from the Lidar sensor, improving its performance. |
Keywords
» Artificial intelligence » Distillation » Knowledge distillation » Object detection » Semi supervised
