少数ショット物体検出のための高速階層学習
Fast Hierarchical Learning for Few-Shot Object Detection
少数ショット物体検出を階層学習問題として捉え、新規クラスを既存の基底クラスの子クラスとして扱い、専用の最適化戦略で検出ヘッドを訓練することで、訓練時間を大幅に短縮しつつ基底クラスの性能を維持する手法を提案した。
著者: Yihang She, Goutam Bhat, Martin Danelljan, Fisher Yu
分類: cs.CV, cs.RO
原文アブストラクト
Transfer learning based approaches have recently achieved promising results on the few-shot detection task. These approaches however suffer from ``catastrophic forgetting'' issue due to finetuning of base detector, leading to sub-optimal performance on the base classes. Furthermore, the slow convergence rate of stochastic gradient descent (SGD) results in high latency and consequently restricts real-time applications. We tackle the aforementioned issues in this work. We pose few-shot detection as a hierarchical learning problem, where the novel classes are treated as the child classes of existing base classes and the background class. The detection heads for the novel classes are then trained using a specialized optimization strategy, leading to significantly lower training times compared to SGD. Our approach obtains competitive novel class performance on few-shot MS-COCO benchmark, while completely retaining the performance of the initial model on the base classes. We further demonstrate the application of our approach to a new class-refined few-shot detection task.
