Open-set object detection (OSOD) aims to detect the known categories and reject unknown objects in a dynamic world, which has achieved significant attention. However, previous approaches only consider this problem in data-abundant conditions, while neglecting the few-shot scenes. In this paper, we seek a solution for the generalized few-shot open-set object detection (G-FOOD), which aims to avoid detecting unknown classes as known classes with a high confidence score while maintaining the performance of few-shot detection. The main challenge for this task is that few training samples induce the model to overfit on the known classes, resulting in a poor open-set performance. We propose a new G-FOOD algorithm to tackle this issue, named \underline{F}ew-sh\underline{O}t \underline{O}pen-set \underline{D}etector (FOOD), which contains a novel class weight sparsification classifier (CWSC) and a novel unknown decoupling learner (UDL). To prevent over-fitting, CWSC randomly sparses parts of the normalized weights for the logit prediction of all classes, and then decreases the co-adaptability between the class and its neighbors. Alongside, UDL decouples training the unknown class and enables the model to form a compact unknown decision boundary. Thus, the unknown objects can be identified with a confidence probability without any threshold, prototype, or generation. We compare our method with several state-of-the-art OSOD methods in few-shot scenes and observe that our method improves the F-score of unknown classes by 4.80\%-9.08\% across all shots in VOC-COCO dataset settings \footnote[1]{The source code is available at \url{https://github.com/binyisu/food}}.

翻译：开集目标检测旨在动态世界中检测已知类别并拒绝未知目标，该任务已获得广泛关注。然而，现有方法仅考虑数据充足条件下的开集检测问题，忽视了少样本场景。本文致力于解决广义少样本开集目标检测问题，其目标是在保持少样本检测性能的同时，避免将未知类以高置信度误检为已知类。该任务的主要挑战在于：少量训练样本易导致模型对已知类过拟合，进而削弱开集性能。为此，我们提出一种新型G-FOOD算法——少样本开集检测器（FOOD），其包含创新性的类别权重稀疏化分类器（CWSC）与未知解耦学习器（UDL）。为预防过拟合，CWSC通过随机稀疏化所有类别的对数几率预测归一化权重，降低类别与其邻近类别的共适应性；同时，UDL将未知类训练过程解耦，使模型形成紧凑的未知类别决策边界。由此，无需设定阈值、原型或生成式方法，即可通过置信概率识别未知目标。我们将该方法与多种前沿开集目标检测方法在少样本场景下进行对比，实验结果表明，在VOC-COCO数据集设置下，本方法在所有样本数量条件下使未知类的F值提升4.80%-9.08%。