The past few years in the fields of Person Re-Identification (RE-ID) have seen attention mechanism receives enormous interest as it has superior performance in obtaining discriminative feature representations. However, a wide range of state-of-the-art RE-ID attention models only focus on one-dimensional attention design method, e.g. spatial attention and channels attention, hence the produced attention maps are neither detailed enough nor discriminative enough to capture complicated interactions of visual parts. Developing multi-scale attention mechanism for RE-ID, an under-studied approach, becomes a practicable method to overcome this deficiency. Toward this goal, we propose a Multiscale Omnibearing Attention Networks (MOAN) for RE-ID which is capable of utilizing the complex fusion information acquired from the multiscale attention mechanism with features being more representative. Specifically, MOAN takes full advantage of multi-sized convolution filters to obtain discriminative holistic and local feature maps, and adaptively conducts feature information augmentation by introducing an Omnibearing Attention (OA) module. Through the OA module, spatial attention and channel attention are integrated together in a unique way where they work in a complementary way. To sum up, MOAN not only inherits the merit of two kinds of attention mechanism but also performs well in extracting comprehensive feature information. Furthermore, taking into account the robustness of model performance, we formulate a Random Drop (RD) Function to facilitate training MOAN and further increase the diversity of training model for adaptation. Furthermore, to achieve end-to-end training, we utilize trainable parameters to take place of initial fixed parameters, and the model performance is experimentally promoted. Extensive experiments have been carried out on the four mainstream RE-ID datasets. As the result shows, our method with re-ranking achieves rank-1 accuracy of 92.29% on CUHK03-NP, 97.45% on Market-1501, 93.81% on DukeMTMC-reID and 81.53% on MSMT17-V2, outperforming the state-of-the-art methods and confirming the effectiveness of our method.

中文翻译：

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用于人员重新识别的多尺度全方位注意网络

过去几年，在人员重新识别（RE-ID）领域中，注意力机制受到了极大的关注，因为它在获取歧视性特征表示方面具有卓越的性能。但是，各种各样的最新RE-ID注意模型仅关注一维注意设计方法，例如空间注意和通道注意，因此生成的注意图既不够详尽也不具有区分性，无法捕获复杂的内容。视觉部分的相互作用。为RE-ID开发一种多尺度关注机制（一种尚未被充分研究的方法）成为克服这一缺陷的可行方法。为了实现这个目标，我们提出了一种用于RE-ID的多尺度全方位注意力网络（MOAN），该网络能够利用从多尺度注意力机制获取的复杂融合信息，并且具有更具代表性的特征。具体而言，MOAN充分利用了多尺寸卷积滤波器来获得判别性整体特征图和局部特征图，并通过引入全方位关注（OA）模块来自适应地进行特征信息增强。通过OA模块，空间注意力和渠道注意力以独特的方式整合在一起，从而以互补的方式发挥作用。综上所述，MOAN不仅继承了两种注意力机制的优点，而且在提取综合特征信息方面也表现出色。此外，考虑到模型性能的稳健性，我们制定了随机丢弃（RD）函数，以方便训练MOAN，并进一步提高训练模型的适应性。此外，为了实现端到端训练，我们利用可训练的参数代替初始固定参数，并通过实验提高了模型性能。已经对四个主流RE-ID数据集进行了广泛的实验。结果表明，我们的重新排序方法在CUHK03-NP上的等级1精度达到92.29％，在Market-1501上达到97.45％，在DukeMTMC-reID上达到93.81％，在MSMT17-V2上达到81.53％，优于先进的方法，并证实了我们方法的有效性。我们利用可训练的参数代替初始的固定参数，并通过实验提高了模型的性能。已经对四个主流RE-ID数据集进行了广泛的实验。结果表明，我们的重新排序方法在CUHK03-NP上的等级1精度达到92.29％，在Market-1501上达到97.45％，在DukeMTMC-reID上达到93.81％，在MSMT17-V2上达到81.53％，优于先进的方法，并证实了我们方法的有效性。我们利用可训练的参数代替初始的固定参数，并通过实验提高了模型的性能。已经对四个主流RE-ID数据集进行了广泛的实验。结果表明，我们的重新排序方法在CUHK03-NP上的等级1精度达到92.29％，在Market-1501上达到97.45％，在DukeMTMC-reID上达到93.81％，在MSMT17-V2上达到81.53％，优于先进的方法，并证实了我们方法的有效性。