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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2409.10716 |
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| _version_ | 1866916397147226112 |
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| author | Jian, Yanan Yu, Fuxun Zhang, Qi Levine, William Dubbs, Brandon Karianakis, Nikolaos |
| author_facet | Jian, Yanan Yu, Fuxun Zhang, Qi Levine, William Dubbs, Brandon Karianakis, Nikolaos |
| contents | This paper presents a novel way of online adapting any off-the-shelf object detection model to a novel domain without retraining the detector model. Inspired by how humans quickly learn knowledge of a new subject (e.g., memorization), we allow the detector to look up similar object concepts from memory during test time. This is achieved through a retrieval augmented classification (RAC) module together with a memory bank that can be flexibly updated with new domain knowledge. We experimented with various off-the-shelf open-set detector and close-set detectors. With only a tiny memory bank (e.g., 10 images per category) and being training-free, our online learning method could significantly outperform baselines in adapting a detector to novel domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10716 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online Learning via Memory: Retrieval-Augmented Detector Adaptation Jian, Yanan Yu, Fuxun Zhang, Qi Levine, William Dubbs, Brandon Karianakis, Nikolaos Computer Vision and Pattern Recognition Information Retrieval Machine Learning This paper presents a novel way of online adapting any off-the-shelf object detection model to a novel domain without retraining the detector model. Inspired by how humans quickly learn knowledge of a new subject (e.g., memorization), we allow the detector to look up similar object concepts from memory during test time. This is achieved through a retrieval augmented classification (RAC) module together with a memory bank that can be flexibly updated with new domain knowledge. We experimented with various off-the-shelf open-set detector and close-set detectors. With only a tiny memory bank (e.g., 10 images per category) and being training-free, our online learning method could significantly outperform baselines in adapting a detector to novel domains. |
| title | Online Learning via Memory: Retrieval-Augmented Detector Adaptation |
| topic | Computer Vision and Pattern Recognition Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2409.10716 |