  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.
