Summary of Cst: Calibration Side-tuning For Parameter and Memory Efficient Transfer Learning, by Feng Chen
CST: Calibration Side-Tuning for Parameter and Memory Efficient Transfer Learning
by Feng Chen
First submitted to arxiv on: 20 Feb 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 paper presents a novel approach to object detection in resource-constrained environments. While most research focuses on detecting specific classes of objects, achieving high accuracy across all classes remains a challenge. To address this, the authors propose Calibration side tuning, a lightweight fine-tuning strategy that adapts successful transformer techniques for use with ResNet architectures. The approach incorporates maximal transition calibration and uses a small number of additional parameters to enhance network performance while maintaining a smooth training process. The paper also conducts an analysis of multiple fine-tuning strategies and applies them within ResNet, expanding the research on finetune schemes for object detection networks. Experimental results demonstrate that Calibration side tuning outperforms state-of-the-art techniques, achieving a better balance between complexity and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us detect objects more accurately even when we have limited computer power and storage space. Usually, we focus on finding specific types of objects, but this isn’t easy for all object classes. The authors found a way to make object detection work better in these situations by using a special technique called Calibration side tuning. This method uses ideas from transformers to improve ResNet architectures. It works well even with limited resources and can be used for many different object detection tasks. The paper also compares this new approach with other methods and shows that it’s better at finding objects accurately while keeping the calculations simple. |
Keywords
» Artificial intelligence » Fine tuning » Object detection » Resnet » Transformer
