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.

利用 Sylph 的超网络结构，通过将目标分类与定位分离，我们在增量式 few-shot 目标检测中取得了很高的准确性，尤其是当有大量基础类别可用于元训练时，没有忘记先前学习到的类别。

Sylph: 一种增量式少样本物体检测的超网络框架