Face clustering tasks can learn hierarchical semantic information from
large-scale data, which has the potential to help facilitate face recognition.
However, there are few works on this problem. This paper explores it by
proposing a joint optimization task of label classification and supervised
contrastive clustering to introduce the cluster knowledge to the traditional
face recognition task in two ways. We first extend ArcFace with a
cluster-guided angular margin to adjust the within-class feature distribution
according to the hard level of face clustering. Secondly, we propose a
supervised contrastive clustering approach to pull the features to the cluster
center and propose the cluster-aligning procedure to align the cluster center
and the learnable class center in the classifier for joint training. Finally,
extensive qualitative and quantitative experiments on popular facial benchmarks
demonstrate the effectiveness of our paradigm and its superiority over the
existing approaches to face recognition.

本研究论文探索了面部聚类任务在从大规模数据中学习层次结构语义信息方面的潜力，并通过提出标签分类和监督对比聚类的联合优化任务的两种方式将聚类知识引入传统面部识别任务。通过将簇引导的角度边界扩展到 ArcFace 来根据面部聚类的难度级别调整类内特征分布，并提出了一种监督对比聚类方法来将特征拉向簇中心，并通过簇对准过程将簇中心与可学习的类中心在分类器中对齐进行联合训练。最后，对流行的面部基准进行了广泛的定性和定量实验，证明了我们范式的有效性以及在现有的面部识别方法上的优越性。

通过联合标签分类和受监督对比聚类改进面部表示

Improved Face Representation via Joint Label Classification and  Supervised Contrastive Clustering

Siamese networks have gained popularity as a method for modeling text
semantic similarity. Traditional methods rely on pooling operation to compress
the semantic representations from Transformer blocks in encoding, resulting in
two-dimensional semantic vectors and the loss of hierarchical semantic
information from Transformer blocks. Moreover, this limited structure of
semantic vectors is akin to a flattened landscape, which restricts the methods
that can be applied in downstream modeling, as they can only navigate this flat
terrain. To address this issue, we propose a novel 3D Siamese network for text
semantic similarity modeling, which maps semantic information to a
higher-dimensional space. The three-dimensional semantic tensors not only
retains more precise spatial and feature domain information but also provides
the necessary structural condition for comprehensive downstream modeling
strategies to capture them. Leveraging this structural advantage, we introduce
several modules to reinforce this 3D framework, focusing on three aspects:
feature extraction, attention, and feature fusion. Our extensive experiments on
four text semantic similarity benchmarks demonstrate the effectiveness and
efficiency of our 3D Siamese Network.

我们提出了一种新颖的 3D 孪生网络模型，用于文本语义相似性建模，通过将语义信息映射到高维空间，保留了更精确的空间和特征领域信息，并为综合下游建模策略提供了必要的结构条件。通过引入多个模块来增强这一 3D 框架，包括特征提取、注意力和特征融合，在四个文本语义相似性基准上的广泛实验证明了我们 3D 孪生网络的有效性和效率。