1. Online Learning via Memory: Retrieval-Augmented Detector Adaptation
- Author
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Jian, Yanan, Yu, Fuxun, Zhang, Qi, Levine, William, Dubbs, Brandon, and Karianakis, Nikolaos
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Information Retrieval ,Computer Science - Machine Learning - Abstract
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., Comment: Accepted at ECCV 2024, Human-Inspired Computer Vision (HCV) workshop
- Published
- 2024