ID 原文 译文
38876 实验表明，该方法可以显著提高图像检索的性能。 The experimental results demonstrate that the proposed method can significantly improve the performance of image retrieval.
38877 现有的目标再辨识方法常用全局特征池化层来聚合深度骨干网络所提取的特征映射以得到最终的图像特征。 Existing object re-identification methods usually use global feature pooling layers to aggregate feature maps extracted via deep backbone networks to obtain the final image features.
38878 但是，全局特征池化层忽视了特征映射在空间和通道上的显著性，会限制所得图像特征的鉴别能力。 However, global feature pooling layers ignore the spatial and channel significance of feature maps, restricting the discriminative ability of the final image features.
38879 为此，本文设计一个新颖的空间和通道双重显著性挖掘(Spatial Channel Dual Significance Mining， SC-DSM)模块，用于同时从空间和通道两个维度上充分挖掘特征映射的显著性，从而改善所得图像特征的鉴别能力，以提升目标再辨识的准确性。 For that, in this paper, a spatial channel dual significance mining(SC-DSM) module is designed to comprehensively exploit the feature map's significance from both spatial and channel dimensions to enhance the feature's discriminative ability, thereby improving the object re-identification accuracy.
38880 SC-DSM模块包含空间显著性挖掘子模块和通道显著性挖掘子模块。 The SC-DSM module consists of a spatial significance mining(SSM) sub-module and a channel significance mining(CSM) sub-module.
38881 其中，空间显著性挖掘子模块在特征映射上构建空间图，聚合空间维度上的邻居节点特征并学习权重，实现空间显著性挖掘;通道显著性挖掘子模块在特征映射建立通道图聚合通道维度上的邻居节点并学习权重，实现通道显著性挖掘。 On feature maps, the SSM sub-module builds spatial graphs to aggregate spatial neighbor nodes' features and learn weights to exploit the spatial significance, while the CSM sub-module constructs channel graphs to aggregate channel neighbor nodes' features and learn weights to mine the channel significance.
38882 实验结果表明，在目前最流行的车辆再辨识数据库VeRi776和行人再辨识数据库Market-1501上，所提出的方法能够优于现有的目标再辨识方法。 Experimental results show that proposed method can outperform existing object re-identification methods on the most popular vehicle re-identification dataset, namely, VeRi776 and person re-identification dataset, namely, Market-1501.
38883 鉴于ResNet的强大表达能力，其在行人重识别领域获得了广泛的应用。 Due to its strong expressive ability, ResNet is widely used in Person Re-Identification.
38884 虽然基于ResNet50构建的行人重识别网络取得了优异的性能，但流行的ResNet50仍存在模型体积大、效率低等局限性。 Although the use of ResNet50 for person ReID has achieved excellent performance, the popular ResNet-50-based solution still has the limitations of large model size and low efficiency in implementations.
38885 与之相比，EfficientNet作为一种新兴的深度模型，具有设计合理、运行高效等特点，并在ImageNet数据集上有着更出色的性能表现。 Recently, EfficientNet was proposed as an emerging alternative for design of deep neural models. Compared with ResNet-50, EfficientNet, as an emerging depth model, has the characteristics of reasonable design, running efficiently and so on, and has more excellent performance in ImageNet data set.