Summary of Stability Plasticity Decoupled Fine-tuning For Few-shot End-to-end Object Detection, by Yuantao Yin et al.
Stability Plasticity Decoupled Fine-tuning For Few-shot end-to-end Object Detection
by Yuantao Yin, Ping Yin
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 approach aims to improve few-shot object detection (FSOD) by addressing the implicit stability-plasticity contradiction among different modules in object detectors. Specifically, it focuses on the end-to-end object detector Sparse R-CNN and proposes a new three-stage fine-tuning procedure that includes an additional plasticity classifier fine-tuning (PCF) stage to mitigate this issue. The approach also designs a multi-source ensemble (ME) technique to enhance model generalization in the final fine-tuning stage. Experimental results demonstrate the effectiveness of the proposed method in regularizing Sparse R-CNN, outperforming previous methods in FSOD benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FSOD tries to make object detectors work well with just a few labeled examples. Fine-tuning is a good way to do this. But some earlier approaches didn’t think about how different parts of the detector need different things – stability or plasticity. This paper looks at one kind of detector called Sparse R-CNN and finds that it has this problem too. They come up with a new way to fine-tune it, adding an extra step to help the model learn better. They also create a special technique to make sure the model doesn’t overfit. The results show that their approach works well and is better than earlier methods. |
Keywords
» Artificial intelligence » Cnn » Few shot » Fine tuning » Generalization » Object detection
