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.

通过深度度学习，优化深度神经网络的目标函数在创建输入数据的增强特征表示中起着至关重要的作用。然而，基于交叉熵的损失函数对于具有大的类内差异和低的类间差异的输入数据分布来说往往不够充分。深度度量学习旨在通过学习将数据样本映射到代表性嵌入空间的表示函数，以度量数据样本之间的相似性。它利用精心设计的采样策略和损失函数，帮助优化产生具有低类间和高类内方差的区分性嵌入空间的生成。在本章中，我们将概述该领域最新的进展，并讨论最先进的深度度量学习方法。

计算机视觉中的深度度量学习概述

Deep Metric Learning for Computer Vision: A Brief Overview

Gait recognition is a unique biometric technique that can be performed at a
long distance non-cooperatively and has broad applications in public safety and
intelligent traffic systems. Previous gait works focus more on minimizing the
intra-class variance while ignoring the significance in constraining
inter-class variance. To this end, we propose a generalized inter-class loss
which resolves the inter-class variance from both sample-level feature
distribution and class-level feature distribution. Instead of equal penalty
strength on pair scores, the proposed loss optimizes sample-level inter-class
feature distribution by dynamically adjusting the pairwise weight. Further, in
class-level distribution, generalized inter-class loss adds a constraint on the
uniformity of inter-class feature distribution, which forces the feature
representations to approximate a hypersphere and keep maximal inter-class
variance. In addition, the proposed method automatically adjusts the margin
between classes which enables the inter-class feature distribution to be more
flexible. The proposed method can be generalized to different gait recognition
networks and achieves significant improvements. We conduct a series of
experiments on CASIA-B and OUMVLP, and the experimental results show that the
proposed loss can significantly improve the performance and achieves the
state-of-the-art performances.

提出了一种广义的跨类损失，用于解决人体行走的生物特征识别中的特征分布和性能问题，并在 CASIA-B 和 OUMVLP 上进行实验，取得了显著的性能提升。

步态识别通用跨类别损失

Generalized Inter-class Loss for Gait Recognition

Emerging interests have been brought to recognize previously unseen objects
given very few training examples, known as few-shot object detection (FSOD).
Recent researches demonstrate that good feature embedding is the key to reach
favorable few-shot learning performance. We observe object proposals with
different Intersection-of-Union (IoU) scores are analogous to the intra-image
augmentation used in contrastive approaches. And we exploit this analogy and
incorporate supervised contrastive learning to achieve more robust objects
representations in FSOD. We present Few-Shot object detection via Contrastive
proposals Encoding (FSCE), a simple yet effective approach to learning
contrastive-aware object proposal encodings that facilitate the classification
of detected objects. We notice the degradation of average precision (AP) for
rare objects mainly comes from misclassifying novel instances as confusable
classes. And we ease the misclassification issues by promoting instance level
intra-class compactness and inter-class variance via our contrastive proposal
encoding loss (CPE loss). Our design outperforms current state-of-the-art works
in any shot and all data splits, with up to +8.8% on standard benchmark PASCAL
VOC and +2.7% on challenging COCO benchmark. Code is available at: https:
//github.com/MegviiDetection/FSCE

本文提出了一种基于监督对比学习的小样本目标检测算法，利用提出的物体与背景对比度来学习物体表示，并通过增强对比密度来提高检测性能，实现了对检测目标的更准确定位和识别。