Summary of Rethinking Feature Backbone Fine-tuning For Remote Sensing Object Detection, by Yechan Kim and Jonghyun Park and Sooyeon Kim and Moongu Jeon
Rethinking Feature Backbone Fine-tuning for Remote Sensing Object Detection
by Yechan Kim, JongHyun Park, SooYeon Kim, Moongu Jeon
First submitted to arxiv on: 21 Jul 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 The proposed DBF (Dynamic Backbone Freezing) method for remote sensing object detection improves performance while reducing computational costs. By introducing a ‘Freezing Scheduler’ module, the approach dynamically manages backbone feature updates during training, allowing for more accurate model learning and efficient processing. The method is evaluated on DOTA and DIOR-R datasets, demonstrating its effectiveness in this domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to detect objects in satellite images uses an “on/off” switch for the main feature extractor (backbone). This helps the model learn better features while using less computer power. The idea works well on two important test sets: DOTA and DIOR-R. This simple and easy-to-implement approach can be used without extra effort to make remote sensing object detection more accurate. |
Keywords
» Artificial intelligence » Object detection
