Deep Metric Learning for Computer Vision: A Brief Overview

• URL: http://arxiv.org/abs/2312.10046v1
• Date: Fri, 1 Dec 2023 21:53:36 GMT
• Title: Deep Metric Learning for Computer Vision: A Brief Overview
• Authors: Deen Dayal Mohan, Bhavin Jawade, Srirangaraj Setlur, Venu Govindaraj
• Abstract summary: Objective functions that optimize deep neural networks play a vital role in creating an enhanced feature representation of the input data. Deep Metric Learning seeks to develop methods that aim to measure the similarity between data samples. We will provide an overview of recent progress in this area and discuss state-of-the-art Deep Metric Learning approaches.
• Score: 4.980117530293724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective functions that optimize deep neural networks play a vital role in creating an enhanced feature representation of the input data. Although cross-entropy-based loss formulations have been extensively used in a variety of supervised deep-learning applications, these methods tend to be less adequate when there is large intra-class variance and low inter-class variance in input data distribution. Deep Metric Learning seeks to develop methods that aim to measure the similarity between data samples by learning a representation function that maps these data samples into a representative embedding space. It leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminative embedding space even for distributions having low inter-class and high intra-class variances. In this chapter, we will provide an overview of recent progress in this area and discuss state-of-the-art Deep Metric Learning approaches.

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