Deep Metric Learning with Hierarchical Triplet Loss

Weifeng Ge; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 269-285

We present a novel hierarchical triplet loss (HTL) capable of automatically collecting informative training samples (triplets) via a defined hierarchical tree that encodes global context information. This allows us to cope with the main limitation of random sampling in training a conventional triplet loss, which is a central issue for deep metric learning. Our main contributions are two-fold. (i) we construct a hierarchical class-level tree where neighboring classes are merged recursively. The hierarchical structure naturally captures the intrinsic data distribution over the whole dataset. (ii) we formulate the problem of triplet collection by introducing a new violate margin, which is computed dynamically based on the designed hierarchical tree. This allows it to automatically select meaningful hard samples with the guide of global context. It encourages the model to learn more discriminative features from visual similar classes, leading to faster convergence and better performance. In addition, the proposed HTL is easily implemented, and the new violate margin can be readily integrated into the standard triplet loss and other deep metric learning functions. Our method is evaluated on the tasks of image retrieval and face recognition, where it can obtain comparable performance with much fewer iterations. It outperforms the standard triplet loss substantially by 1% - 18%, and achieves new state-of-the-art performance on a number of benchmarks.