Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach
Tae-Min Choi,Jong-Hwan Kim,Tae-Min Choi,Jong-Hwan Kim
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 metalearning. However, meta-learning approaches show insufficient performance that is difficult to apply to practical problems. In this light, we propose a simple fine...
