Learning Local Descriptors With a CDF-Based Dynamic Soft Margin

Linguang Zhang, Szymon Rusinkiewicz; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 2969-2978

Abstract


The triplet loss is adopted by a variety of learning tasks, such as local feature descriptor learning. However, its standard formulation with a hard margin only leverages part of the training data in each mini-batch. Moreover, the margin is often empirically chosen or determined through computationally expensive validation, and stays unchanged during the entire training session. In this work, we propose a simple yet effective method to overcome the above limitations. The core idea is to replace the hard margin with a non-parametric soft margin, which is dynamically updated. The major observation is that the difficulty of a triplet can be inferred from the cumulative distribution function of the triplets' signed distances to the decision boundary. We demonstrate through experiments on both real-valued and binary local feature descriptors that our method leads to state-of-the-art performance on popular benchmarks, while eliminating the need to determine the best margin.

Related Material


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[bibtex]
@InProceedings{Zhang_2019_ICCV,
author = {Zhang, Linguang and Rusinkiewicz, Szymon},
title = {Learning Local Descriptors With a CDF-Based Dynamic Soft Margin},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}