In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes. Previous iFSD works achieved the desired results by applying meta-learning. However, meta-learning approaches show insufficient performance that is difficult to apply to practical problems. In this light, we propose a simple fine-tuning-based approach, the Incremental Two-stage Fine-tuning Approach (iTFA) for iFSD, which contains three steps: 1) base training using abundant base classes with the class-agnostic box regressor, 2) separation of the RoI feature extractor and classifier into the base and novel class branches for preserving base knowledge, and 3) fine-tuning the novel branch using only a few novel class examples. We evaluate our iTFA on the real-world datasets PASCAL VOC, COCO, and LVIS. iTFA achieves competitive performance in COCO and shows a 30% higher AP accuracy than meta-learning methods in the LVIS dataset. Experimental results show the effectiveness and applicability of our proposed method.

翻译：本文探讨了增量式少样本目标检测（iFSD），该方法通过仅使用少量示例增量学习新类别，无需重新访问基类。以往iFSD研究通过应用元学习取得了预期成果，但元学习方法表现出难以应用于实际问题的不足。为此，我们提出了一种基于简单微调的方法——增量式两阶段微调方法（iTFA），用于iFSD，该方法包含三个步骤：1）使用丰富基类训练基类，并采用类别无关的边界框回归器；2）将RoI特征提取器和分类器分离为基类和新类别分支，以保留基类知识；3）仅使用少量新类别示例微调新类别分支。我们在真实数据集PASCAL VOC、COCO和LVIS上评估了iTFA。iTFA在COCO上取得了有竞争力的性能，并在LVIS数据集上比元学习方法AP精度高出30%。实验结果表明了我们提出方法的有效性和适用性。