We propose Re-parameterized Refocusing Convolution (RefConv) as a replacement for regular convolutional layers, which is a plug-and-play module to improve the performance without any inference costs. Specifically, given a pre-trained model, RefConv applies a trainable Refocusing Transformation to the basis kernels inherited from the pre-trained model to establish connections among the parameters. For example, a depth-wise RefConv can relate the parameters of a specific channel of convolution kernel to the parameters of the other kernel, i.e., make them refocus on the other parts of the model they have never attended to, rather than focus on the input features only. From another perspective, RefConv augments the priors of existing model structures by utilizing the representations encoded in the pre-trained parameters as the priors and refocusing on them to learn novel representations, thus further enhancing the representational capacity of the pre-trained model. Experimental results validated that RefConv can improve multiple CNN-based models by a clear margin on image classification (up to 1.47% higher top-1 accuracy on ImageNet), object detection and semantic segmentation without introducing any extra inference costs or altering the original model structure. Further studies demonstrated that RefConv can reduce the redundancy of channels and smooth the loss landscape, which explains its effectiveness.

翻译：本文提出一种可替代常规卷积层的参数重聚焦卷积（RefConv），它是一种即插即用模块，无需额外推理成本即可提升模型性能。具体而言，给定预训练模型，RefConv对继承自预训练模型的基础卷积核施加可训练的重聚焦变换，以建立参数间的联系。例如，深度可分离RefConv能将特定通道卷积核的参数与其他通道核参数相关联，使其关注原本未曾关注的模型其他部分，而非仅聚焦于输入特征。从另一视角看，RefConv通过利用预训练参数编码的表征作为先验知识并对其进行重聚焦学习新表征，从而增强现有模型结构的先验信息，进而提升预训练模型的表征能力。实验结果表明，RefConv能在图像分类（ImageNet上最高提升1.47%的Top-1准确率）、目标检测和语义分割任务中显著改进多种CNN模型，且无需引入额外推理成本或改变原始模型结构。进一步研究证实，RefConv能减少通道冗余并平滑损失景观，这解释了其有效性机制。