Feature disentanglement in one-stage object detection

Abstract

In this paper, an enhanced disentanglement module is proposed to address feature misalignment caused by inherently irreconcilable conflicts between classification and regression tasks in Convolutional Neural Network-based object detectors. The proposed method disentangles features in the feature pyramid network (FPN) at the neck of the architecture. In addition, a response alignment strategy is proposed to reduce inconsistent responses and suppress inferior predictions. Extensive experiments are performed on the MS COCO and PASCAL VOC datasets with different backbones, confirming that the proposed method improves performance significantly. The proposed method exhibits two main advantages over existing solutions—features are disentangled at the neck instead of at the head, enabling comprehensive resolution of feature misalignment, and independent outputs of the two tasks after feature disentanglement are avoided, thereby preventing response inconsistencies.