Summary of Latent Distillation For Continual Object Detection at the Edge, by Francesco Pasti et al.
Latent Distillation for Continual Object Detection at the Edge
by Francesco Pasti, Marina Ceccon, Davide Dalle Pezze, Francesco Paissan, Elisabetta Farella, Gian Antonio Susto, Nicola Bellotto
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research paper tackles the challenge of adapting object detection models to new data while maintaining their performance on previous data, a crucial issue in edge devices like those used in automotive and robotics. To address this, the authors investigate the suitability of NanoDet, an open-source, lightweight, and fast detector for continual learning on edge devices. The paper also proposes a novel method called Latent Distillation (LD) that reduces memory and computation requirements without compromising detection performance. This approach is validated using well-known benchmarks like VOC and COCO, achieving significant reductions in distillation parameter overhead and Floating Point Operations per model update compared to other distillation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in artificial intelligence: how to make object detection models work better when they’re given new data. It’s especially important for devices like those used in cars or robots that need to adapt quickly. The researchers look at a special type of model called NanoDet and try to figure out if it can be used for this purpose. They also come up with a new way to make the model learn faster without using too much memory or computer power. This works well on famous benchmark tests, making it useful for real-world applications. |
Keywords
» Artificial intelligence » Continual learning » Distillation » Object detection
