Summary of Source-free Domain Adaptation For Yolo Object Detection, by Simon Varailhon et al.
Source-Free Domain Adaptation for YOLO Object Detection
by Simon Varailhon, Masih Aminbeidokhti, Marco Pedersoli, Eric Granger
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper proposes a novel domain adaptation technique called Source-Free YOLO (SF-YOLO) for object detection in real-world vision systems. The approach is designed for the YOLO family of detectors, which are known for their fast baselines and practical applications. SF-YOLO uses a teacher-student framework, where the student receives images with target domain-specific augmentations, allowing it to be trained solely on unlabeled target data without requiring feature alignment. To address the challenge of self-training using mean-teacher architecture in the absence of labels, the authors introduce a teacher-to-student communication mechanism to stabilize training and reduce reliance on annotated target data for model selection. The proposed method is competitive with state-of-the-art detectors on several challenging benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a tricky problem called source-free domain adaptation in object detection. It’s like taking a picture of a car, but the camera has never seen that type of car before. Most ways to solve this problem are designed for a specific detector called Faster-RCNN, which is very good but also very slow. The authors focus on detectors that are fast and practical, specifically YOLO. They propose a new way to adapt the model using a teacher-student framework, where the student learns from images with special augmentations. This helps the model train without needing any data from the source domain. The method is simple but effective, performing well on challenging datasets. |
Keywords
» Artificial intelligence » Alignment » Domain adaptation » Faster rcnn » Object detection » Self training » Yolo
