CDANet: Common-and-Differential Attention Network for Object Detection and Instance Segmentation

Abstract

• A novel Common-and-Differential Attention Network (CDANet) is proposed to enhance feature representations. • Common-and-Differential operations are introduced to restrain redundant and confusing background interference. • The effectiveness of CDANet is validated on object detection and instance segmentation tasks . In this paper, we propose a simple and effective C ommon-and- D ifferential A ttention N etwork ( CDANet ) for object detection and instance segmentation. For an input intermediate feature map, CDANet infers parallelly attention modules along channel and spatial dimensions respectively, then both attention modules are multiplied with the input feature map for the refined features. Specially, since redundant and confusing background may misdirect the localization at object boundary, our attention network applies Common-and-Differential operations to weaken useless background interference and focus on meaningful object features. The proposed CDANet is verified performance through comprehensive experiments on PASCAL VOC2007 and MS COCO2017 datasets for object detection and instance segmentation tasks. CDANet obtains consistently improved results on various detectors with different backbones, indicating the significant effectiveness and applicability of CDANet.