Deep Metric Learning with Hierarchical Triplet Loss

TL;DR

This paper introduces a hierarchical triplet loss that automatically selects informative training triplets using a class hierarchy, improving deep metric learning for image retrieval and face recognition.

Contribution

The paper proposes a hierarchical triplet loss with a dynamic violate margin based on a class hierarchy, enhancing triplet sampling and discriminative feature learning.

Findings

Outperforms standard triplet loss by 1%-18% in image retrieval and face recognition.

Achieves state-of-the-art results with fewer training iterations.

Enables faster convergence and better discriminative features.

Abstract

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 database. (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. Our method is evaluated on the tasks of image retrieval and face recognition, where it outperforms the standard triplet loss substantially by 1%-18%. It achieves new state-of-the-art performance on a number of benchmarks, with much fewer learning iterations.