# An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification
However, most of these methods have not dealt with the integration of global features and local features based on above strategies in ReID task properly, resulting in learning redundant ReID features and higher computation complexity. For the excellent lightweight OSNet [27], it relied on depthwise separable convolution and a novel unified aggregation gate to extract omni-scale features, which achieves state-of-the-art performance on six open person ReID datasets. However, it is limited to learn big block attention features and neglects the inside convolution layer's ones. Moreover, the edge deployment often relies on third parties to accelerate the optimization algorithm to improve the inference speed. Presently, the ReID model is mainly designed around high efficiency and lightweight to meet edge deployment requirements. To address the aforementioned problems and challenges of ReID methods, a novel ReID model, called the Multi-Scale Focusing Attention network (MSFANet), is proposed in this paper, which has three stages of different module sizes. Every stage is composed of a different number of multi-scale attention modules, which can be applied to extract the ReID features of multi-scale receptive fields efficiently. Similar to the lite3*3 module in OSNet [27], we use depthwise separable convolution [14; 28] to reduce model computations and parameters. However, OSNet has only an adaptive aggregation after the multi-scale receptive field module blocks, which do not extract attention features inside each block. There is evidence that elaborative attentions play a crucial role in regulating ReID feature [19; 20; 21; 22; 23; 27]. Therefore, it is hard to obtain more in-depth and robust ReID features in each OSNet's stage. We refer to the SENet [18] structure and design a new lightweight focusing-attention (FA) block, using both the depthwise separable convolution and focusing channel attention module inside each FA block to extract more robust ReID features. Due to the sizes of a person in different cameras are usually quite discriminative, we use more extensive cross-scale ReID features in each MSFA module to reduce the number of branches. Later experimental results have shown that our model design dramatically reduces the model computations and improves mAP and Rank-1 metrics. To alleviate the problems of target occlusion, deformation, blur, etc., the optimal data augmentation method, namely the combination of flipping, cropping, erasing, patching and autoaugment [29], is obtained through a large number of experiments in this paper, which makes MSFANet more robust. Besides, we combine the softmax loss function for classification tasks and the TriHard [30] loss function for hard sample discrimination to build a new fusion loss function. Experiments show that the loss function helps the training process with faster convergence on MSFANet and improves the generalization ability of this model. Our contributions can be summarized as follows:  
* The Multi-Scale Focusing Attention Network (MSFANet), containing receptive fields of different sizes in each stage as focusing attention (FA) block, is proposed to capture multiple scales and highly focusing attention ReID features. It is a novel efficient network module that achieves lower computational complexity and can be flexibly embedded in a person ReID framework. * We design a new fusion loss function to address hard sample discrimination problems, which is a combination of softmax loss and hard triplet loss. It achieves faster convergence and better generalization ability of the proposed MSFANet. * Experiments on the Market1501 [1], DukeMTMC-ReID [31], and two small ReID datasets (VIPeR [32], GRID [33]) show the superiority of the proposed MSFANet over a wide range of state-of-the-art ReID models in terms of model accuracy and computational complexity.