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Beyond modality alignment: Learning part-level representation for visible-infrared person re-identification
Image and Vision Computing ( IF 4.7 ) Pub Date : 2021-02-04 , DOI: 10.1016/j.imavis.2021.104118
Peng Zhang , Qiang Wu , Xunxiang Yao , Jingsong Xu

Visible-Infrared person re-IDentification (VI-reID) aims to automatically retrieve the pedestrian of interest exposed to sensors in different modalities, such as visible camera v.s. infrared sensor. It struggles to learn both modality-invariant and discriminant representations. Unfortunately, existing VI-reID work mainly focuses on tackling the modality difference, which fine-grained level discriminant information has not been well investigated. This causes inferior identification performance. To address the problem, we propose a Dual-Alignment Part-aware Representation (DAPR) framework to simultaneously alleviate the modality bias and mine different level of discriminant representations. Particularly, our DAPR reduces modality discrepancy of high-level features hierarchically by back-propagating reversal gradients from a modality classifier, in order to learn a modality-invariant feature space. And meanwhile, multiple heads of classifiers with the improved part-aware BNNeck are integrated to supervise the network producing identity-discriminant representations w.r.t. both local details and global structures in the learned modality-invariant space. By training in an end-to-end manner, the proposed DAPR produces camera-modality-invariant yet discriminant features1 for the purpose of person matching across modalities. Extensive experiments are conducted on two benchmarks, i.e., SYSU MM01 and RegDB, and the results demonstrate the effectiveness of our proposed method.



中文翻译:

超越模态对齐:学习零件级表示形式以进行可见红外人的重新识别

可见红外人重新识别(VI-reID)的目的是自动检索暴露于不同形式的传感器(例如可见摄像机与红外传感器)中的目标行人。学习模态不变和判别表示都很困难。不幸的是,现有的VI-reID工作主要集中在解决模态差异上,而细粒度的判别信息尚未得到很好的研究。这导致识别性能变差。为了解决该问题,我们提出了一种双重对齐的零件感知表示(DAPR)框架,以同时减轻模态偏差并挖掘不同级别的判别表示。尤其是,我们的DAPR通过向后传播模态分类器的逆向梯度,逐步降低了高级特征的模态差异,为了学习模态不变的特征空间。同时,集成了具有改进的局部感知BNNeck的多个分类器头,以监督网络,从而在学习的模态不变空间中利用局部细节和全局结构来产生身份区分的表示。通过端到端的训练,拟议的DAPR产生了相机模式不变但有区别的特征1,用于跨模式的人员匹配。在SYSU MM01和RegDB这两个基准上进行了广泛的实验,结果证明了该方法的有效性。

更新日期:2021-02-16
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