Summary of Online Learning Via Memory: Retrieval-augmented Detector Adaptation, by Yanan Jian et al.
Online Learning via Memory: Retrieval-Augmented Detector Adaptation
by Yanan Jian, Fuxun Yu, Qi Zhang, William Levine, Brandon Dubbs, Nikolaos Karianakis
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Information Retrieval (cs.IR); 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 proposes an innovative approach for fine-tuning any off-the-shelf object detection model for a new domain without requiring retraining. This is achieved by allowing the detector to draw upon memory of similar object concepts during test time, inspired by human learning patterns. The method combines a retrieval augmented classification (RAC) module with a memory bank that can be updated with new knowledge. Experiments with various off-the-shelf detectors demonstrate significant performance improvements in adapting to novel domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper has found a way to make object detection models work better in new situations without needing to start from scratch. It does this by letting the model look at similar things it has seen before, kind of like how we remember things we learned earlier. This helps the model adapt faster and do better in new places. |
Keywords
* Artificial intelligence * Classification * Fine tuning * Object detection
