Summary of Preventing Catastrophic Forgetting Through Memory Networks in Continuous Detection, by Gaurav Bhatt et al.
Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection
by Gaurav Bhatt, James Ross, Leonid Sigal
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The paper introduces a memory-based detection transformer architecture to adapt pre-trained DETR-style detectors to new tasks while retaining knowledge from previous tasks. The proposed localized query function aims to minimize forgetting, addressing the fundamental challenge of “background relegation” where object categories from earlier tasks reappear in future tasks without labels. The approach surpasses state-of-the-art performance on continual detection benchmarks, achieving 5-7% improvements on MS-COCO and PASCAL-VOC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us build better machine learning models that can learn new things without forgetting what they already know. It creates a special kind of model called a “detection transformer” that can adapt to new tasks while keeping the knowledge it gained from earlier tasks. This is important because sometimes, things we learned before come back in a different way, and our model needs to be able to recognize them again. The approach works really well, beating other state-of-the-art methods on tests. |
Keywords
* Artificial intelligence * Machine learning * Transformer
