利用局部软关注和双交叉邻域标签平滑进行联合训练,实现无监督人员再识别

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Qing Han, Longfei Li, Weidong Min, Qi Wang, Qingpeng Zeng, Shimiao Cui, Jiongjin Chen
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引用次数: 0

摘要

现有的无监督人员再识别方法未能充分捕捉局部区域的细粒度特征,这可能导致外表相似但身份不同的人在聚类后被贴上相同的标签。不同局部区域所包含的与身份无关的信息会导致不同程度的局部噪声。为了解决这些难题,本研究提出了局部软关注和双交叉邻域标签平滑(DCLS)联合训练。首先,联合训练分为全局和局部两部分,其中局部部分采用软注意力机制,以准确捕捉局部区域的细微差别,从而提高再识别模型识别人的局部重要特征的能力。其次,DCLS 是为了逐步减轻不同局部区域的标签噪声而设计的。DCLS 使用全局和局部相似度指标对人物的全局和局部区域进行语义对齐,并通过相邻区域的交叉信息进一步确定局部区域之间的近似关联,从而在整个训练过程中实现全局和局部区域的标签平滑。在大量实验中,所提出的方法在多个标准人物再识别数据集上的无监督设置下的表现优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Joint training with local soft attention and dual cross-neighbor label smoothing for unsupervised person re-identification

Joint training with local soft attention and dual cross-neighbor label smoothing for unsupervised person re-identification

Existing unsupervised person re-identification approaches fail to fully capture the fine-grained features of local regions, which can result in people with similar appearances and different identities being assigned the same label after clustering. The identity-independent information contained in different local regions leads to different levels of local noise. To address these challenges, joint training with local soft attention and dual cross-neighbor label smoothing (DCLS) is proposed in this study. First, the joint training is divided into global and local parts, whereby a soft attention mechanism is proposed for the local branch to accurately capture the subtle differences in local regions, which improves the ability of the re-identification model in identifying a person’s local significant features. Second, DCLS is designed to progressively mitigate label noise in different local regions. The DCLS uses global and local similarity metrics to semantically align the global and local regions of the person and further determines the proximity association between local regions through the cross information of neighboring regions, thereby achieving label smoothing of the global and local regions throughout the training process. In extensive experiments, the proposed method outperformed existing methods under unsupervised settings on several standard person re-identification datasets.

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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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