# Fast Hierarchical Learning for Few-Shot Object Detection Source: [Fast Hierarchical Learning for Few-Shot Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:39:21.820Z Authors: Yihang She, Goutam Bhat, Martin Danelljan, Fisher Yu Published: 2022-10-10T20:31:19.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()