<p>Deep metric learning methods typically design loss functions to learn feature embedding spaces where similar samples get closer and dissimilar samples push farther. The existing multi-proxies losses assign multiple proxies and calculate the weighted similarity between samples and proxies of the same class, which ignores the unique fine-grained representation of each sample. Moreover, simply constraining the relationship between samples and proxies may result in proxies inadvertently converging and blurring the category boundaries throughout the optimization process. This paper proposes a deep metric learning framework that integrates a fine-grained features mining module (FFMM) and an orthogonalization constraint module (OCM). The FFMM combines semantic enhancement to generate multiple proxies for each class, capturing meaningful data. For each sample, it adaptively connects to partial proxies to capture fine-grained feature representations. The OCM enforces orthogonal constraints on the relationships between non-similar proxies. These constraints guide samples to gradually move toward positive proxies while moving away from negative ones. The proposed method aims to guide appropriate movement directions for each sample by generating multiple proxies and capturing fine-grained feature representations. Furthermore, it offers a global constraint on sample movement through proxy orthogonalization. Experiments conducted on three benchmark datasets demonstrate the effectiveness of the proposed method.</p>