Orthogonal Progressive Network for Few-shot Object Detection

Abstract

Few-Shot Object Detection (FSOD) is a significant application of few-shot learning in object detection tasks. Its primary objective is to enable the model to quickly acquire the ability to detect novel categories through a limited number of annotated samples. However, such a small number of training samples is insufficient for the model to be fully trained, which leads to difficulties in completely decoupling the regression and classification tasks, and confusion among categories. To address this issue, this paper proposes to remap features into location and class spaces. By imposing task orthogonality and class orthogonality constraints on the fine-tuning process, task decoupling and class decoupling are achieved. Furthermore, we design and implement a progressive fine-tuning strategy that iteratively optimizes in a lagged and progressive manner through the combination of training samples and historical features, mitigating the risks of catastrophic forgetting and overfitting. Experimental results demonstrate that our method effectively improves the model’s performance in FSOD tasks, achieving state-of-the-art metrics on the PASCAL VOC and MS COCO benchmarks.