We address the problem of distance metric learning in visual similarity
search, defined as learning an image embedding model which projects images into
Euclidean space where semantically and visually similar images are closer and
dissimilar images are further from one another. We present a weakly supervised
adaptive triplet loss (ATL) capable of capturing fine-grained semantic
similarity that encourages the learned image embedding models to generalize
well on cross-domain data. The method uses weakly labeled product description
data to implicitly determine fine grained semantic classes, avoiding the need
to annotate large amounts of training data. We evaluate on the Amazon fashion
retrieval benchmark and DeepFashion in-shop retrieval data. The method boosts
the performance of triplet loss baseline by 10.6% on cross-domain data and
out-performs the state-of-art model on all evaluation metrics.

该研究提出一种弱监督自适应三元组损失方法来解决视觉相似性搜索中的距离度量学习问题，通过使用弱标记的产品描述数据来隐式地确定细粒度语义类别，从而提高了学习图像嵌入模型的泛化能力，并在 Amazon 时尚检索基准和 DeepFashion 店内检索数据集上实现了优于当前最先进模型的性能。