Deep Metric Learning (DML) proposes to learn metric spaces which encode
semantic similarities as embedding space distances. These spaces should be
transferable to classes beyond those seen during training. Commonly, DML
methods task networks to solve contrastive ranking tasks defined over binary
class assignments. However, such approaches ignore higher-level semantic
relations between the actual classes. This causes learned embedding spaces to
encode incomplete semantic context and misrepresent the semantic relation
between classes, impacting the generalizability of the learned metric space. To
tackle this issue, we propose a language guidance objective for visual
similarity learning. Leveraging language embeddings of expert- and
pseudo-classnames, we contextualize and realign visual representation spaces
corresponding to meaningful language semantics for better semantic consistency.
Extensive experiments and ablations provide a strong motivation for our
proposed approach and show language guidance offering significant,
model-agnostic improvements for DML, achieving competitive and state-of-the-art
results on all benchmarks. Code available at
this https URL

采用语言指导目标来进行视觉相似性学习，以更好地实现语义一致性并提升 Deep Metric Learning 的泛化能力。实验证明，语言指导方案可在各项基准测试中实现明显的模型无关性改善，并取得具有竞争力和最先进的结果。