适用于目标检测的上下文感知知识蒸馏网络

针对现有应用于目标检测的知识蒸馏方法难以利用目标周围上下文区域的特征信息,提出适用于目标检测的上下文感知知识蒸馏网络（CAKD Net）方法.该方法能充分利用被检测目标的上下文信息，同时沿空间域和通道域进行信息感知，消除教师网络和学生网络的差异. 该方法包括基于上下文感知的区域提纯模块（CARM）和自适应通道注意力模块（ACAM）. CARM利用上下文信息，自适应生成显著性区域的细粒度掩膜，准确消除教师网络和学生网络各自特征响应在该区域的差异；ACAM引入空间?通道注意力机制，进一步优化目标函数，提高学生网络的性能. 实验结果表明，所提方法对模型检测精确率提升超过2.9%.

Context-aware knowledge distillation network for object detection

A context-aware knowledge distillation network (CAKD Net) method for object detection was proposed, aiming at the current methods of knowledge distillation for the task of object detection were difficult to use feature information of the surrounding context region of the detection object. The context information of the object was fully used, and the gap between the teacher network and the student network were eliminated by performing information perception along the spatial domain and channel domain simultaneously. A context-aware region modified module (CARM) and an adaptive channel attention module (ACAM) were included in CAKD Net. The context information was used to adaptively form a fine-grained mask of the salient region, and the difference of feature response of the teacher network and student network were precisely eliminated in the region of CARM. A novel spatial-channel attention was used to further optimize the objective function, thereby the performance of the student network was improved in ACAM. Experimental results show that the proposed algorithm improves the mean average precision by more than 2.9%.