As an instance-level recognition problem, person re-identification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales. We call these features of both homogeneous and heterogeneous scales omni-scale features. In this paper, a novel deep CNN is designed, termed Omni-Scale Network (OSNet), for omni-scale feature learning in ReID. This is achieved by designing a residual block composed of multiple convolutional feature streams, each detecting features at a certain scale. Importantly, a novel unified aggregation gate is introduced to dynamically fuse multi-scale features with input-dependent channel-wise weights. To efficiently learn spatial-channel correlations and avoid overfitting, the building block uses both pointwise and depthwise convolutions. By stacking such blocks layer-by-layer, our OSNet is extremely lightweight and can be trained from scratch on existing ReID benchmarks. Despite its small model size, our OSNet achieves state-of-the-art performance on six person-ReID datasets.

本文提出了一种新颖的深度学习CNN网络Omni-Scale Network(OSNet), 用于人物再识别问题的判别性特征学习，通过设计一个由多个卷积流组成的残差块来实现不同尺度的特征学习，并引入一个新颖的联合聚合门来动态融合多尺度特征，避免过拟合。在已有的六个人体再识别数据集上，OSNet取得了最新颖的表现，超过了大多数大型模型。

人员再识别的全尺度特征学习