Deep Metric Learning with Hierarchical Triplet Loss
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Face RecognitionImage RetrievalMetric LearningRetrievalTripletMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
Metric learning is one of the techniques in manifold learning with the goal of finding a projection subspace for increasing and decreasing the inter- and intra-class variances, respectively. Some of the metric learning m…
Metric LearningTripletStrong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification
In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID). In general, ReID models are always trained with the sample-based triple…
Cross-Modal Person Re-IdentificationPerson Re-IdentificationTripletRank-based loss for learning hierarchical representations
Hierarchical taxonomies are common in many contexts, and they are a very natural structure humans use to organise information. In machine learning, the family of methods that use the 'extra' information is called hierarc…
Audio ClassificationTripletTripletGAN: Training Generative Model with Triplet Loss
As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the arts. The main innovation of triplet loss i…
Face RecognitionGeneral ClassificationMetric Learningmodel+1Hybrid Losses for Hierarchical Embedding Learning
In traditional supervised learning, the cross-entropy loss treats all incorrect predictions equally, ignoring the relevance or proximity of wrong labels to the correct answer. By leveraging a tree hierarchy for fine-grai…
Multi-Task LearningRetrievalTriplet