用于无监督车辆再识别的内部集群间重组

IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL
Mingkai Qiu;Yuhuan Lu;Xiying Li;Qiang Lu
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引用次数: 0

摘要

最先进的无监督物体再识别(Re-ID)方法使用聚类技术生成的伪标签进行模型训练。遗憾的是,由于车辆再识别中存在标识间相似性和标识内差异性问题,聚类有时会将不同的相似车辆混在一起,或将同一车辆在不同视角下的图像分割成不同的聚类。为了增强模型在此类标签噪声下的 ID 识别能力,我们提出了一种簇内重组方法(ICR),以重组簇内和簇间实例之间的关系,从而在现有聚类结果的基础上提供更高质量的对比学习指导。在簇内重组中,我们设计了一种相机感知最大可靠性子簇组织方法,该方法基于更精细的相机内聚类结果,将每个簇重组为几个质量更高的相交子簇。我们还设计了一种名为 "中心点可靠性 "的新指标来衡量簇内对比学习的可靠性。在簇间重组中,我们提出了一种模糊簇区分标准,用于衡量两个簇属于同一车辆的概率。根据这一标准,我们设计了一种焦点对比损失,以适应性地重新组织模型训练中模糊簇的贡献,从而实现更好的对比学习。在 VeRi-776 和 VERI-Wild 上进行的大量实验证明,ICR 是有效的,可以达到最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inter-Intra Cluster Reorganization for Unsupervised Vehicle Re-Identification
State-of-the-art unsupervised object re-identification (Re-ID) methods conduct model training with pseudo labels generated by clustering techniques. Unfortunately, due to the existence of inter-ID similarity and intra-ID variance problems in vehicle Re-ID, clustering sometimes mixes different similar vehicles together or splits images of the same vehicle in different views into different clusters. To enhance the model’s ID discrimination capability in the presence of such kinds of label noise, we propose an inter-intra cluster reorganization approach (ICR) to reorganize the relationship between instances within and between clusters, which can provide higher-quality contrastive learning guidance based on existing clustering results. In the intra-cluster reorganization, we design a camera-aware maximum reliability sub-cluster organization approach, which reorganizes each cluster into several intersecting sub-clusters of higher quality based on the finer intra-camera clustering results. We further design a novel metric called centroid reliability to measure the reliability of intra-cluster contrastive learning. In the inter-cluster reorganization, we propose an ambiguous cluster discrimination criterion to measure the probability that two clusters belong to the same vehicle. Based on this criterion, we design a focal contrastive loss to adaptively re-organize the contribution of ambiguous clusters in model training to perform better contrastive learning. Extensive experiments on VeRi-776 and VERI-Wild demonstrate that ICR is effective and can achieve state-of-the-art performance.
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来源期刊
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems 工程技术-工程:电子与电气
CiteScore
14.80
自引率
12.90%
发文量
1872
审稿时长
7.5 months
期刊介绍: The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
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