Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection

• URL: http://arxiv.org/abs/2203.13903v1
• Date: Fri, 25 Mar 2022 20:39:00 GMT
• Title: Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection
• Authors: Li Yin, Juan M Perez-Rua, Kevin J Liang
• Abstract summary: We show that finetune-free iFSD can be highly effective when a large number of base categories with abundant data are available for meta-training. We benchmark our model on both COCO and LVIS, reporting as high as $17%$ AP on the long-tail rare classes on LVIS.
• Score: 8.492340530784697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the challenging incremental few-shot object detection (iFSD) setting. Recently, hypernetwork-based approaches have been studied in the context of continuous and finetune-free iFSD with limited success. We take a closer look at important design choices of such methods, leading to several key improvements and resulting in a more accurate and flexible framework, which we call Sylph. In particular, we demonstrate the effectiveness of decoupling object classification from localization by leveraging a base detector that is pretrained for class-agnostic localization on large-scale dataset. Contrary to what previous results have suggested, we show that with a carefully designed class-conditional hypernetwork, finetune-free iFSD can be highly effective, especially when a large number of base categories with abundant data are available for meta-training, almost approaching alternatives that undergo test-time-training. This result is even more significant considering its many practical advantages: (1) incrementally learning new classes in sequence without additional training, (2) detecting both novel and seen classes in a single pass, and (3) no forgetting of previously seen classes. We benchmark our model on both COCO and LVIS, reporting as high as $17\%$ AP on the long-tail rare classes on LVIS, indicating the promise of hypernetwork-based iFSD.

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