Unsupervised deep metric learning (UDML) focuses on learning a semantic
representation space using only unlabeled data. This challenging problem
requires accurately estimating the similarity between data points, which is
used to supervise a deep network. For this purpose, we propose to model the
high-dimensional data manifold using a piecewise-linear approximation, with
each low-dimensional linear piece approximating the data manifold in a small
neighborhood of a point. These neighborhoods are used to estimate similarity
between data points. We empirically show that this similarity estimate
correlates better with the ground truth than the similarity estimates of
current state-of-the-art techniques. We also show that proxies, commonly used
in supervised metric learning, can be used to model the piecewise-linear
manifold in an unsupervised setting, helping improve performance. Our method
outperforms existing unsupervised metric learning approaches on standard
zero-shot image retrieval benchmarks.

无监督深度度量学习通过仅使用无标签数据来学习语义表示空间，其中准确估计数据点之间的相似度是一个具有挑战性的问题。为此，我们提出了使用分段线性逼近来建模高维数据流形的方法，其中每个低维线性段近似于点周围小邻域内的数据流形。通过这些邻域来估计数据点之间的相似度。实验证明，与现有最先进技术的相似度估计相比，我们的方法更好地与真实情况相关。我们还证明，在无监督环境中可以使用代理模型分段线性流形，从而提高性能。我们的方法在标准零样本图像检索基准测试上优于现有的无监督度量学习方法。

深度度量学习的分段线性流形

Piecewise-Linear Manifolds for Deep Metric Learning

The task of searching for visual objects in a large image dataset is
difficult because it requires efficient matching and accurate localization of
objects that can vary in size. Although the segment anything model (SAM) offers
a potential solution for extracting object spatial context, learning embeddings
for local objects remains a challenging problem. This paper presents a novel
unsupervised deep metric learning approach, termed unsupervised collaborative
metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings
for objects of varying scales. Following this, a benchmark of challenges is
assembled by utilizing COCO 2017 and VOC 2007 datasets to facilitate the
training and evaluation of general object retrieval models. Finally, we conduct
comprehensive ablation studies and discuss the complexities faced within the
domain of general object retrieval. Our object retrieval evaluations span a
range of datasets, including BelgaLogos, Visual Genome, LVIS, in addition to a
challenging evaluation set that we have individually assembled for
open-vocabulary evaluation. These comprehensive evaluations effectively
highlight the robustness of our unsupervised MS-UGCML approach, with an object
level and image level mAPs improvement of up to 6.69% and 10.03%, respectively.
The code is publicly available at this https URL

通过无监督的深度度量学习方法，本文提出了一种新颖的混合比例分组的无监督协同度量学习方法 (MS-UGCML)，用于学习不同尺度对象的嵌入表示。同时，通过利用 COCO 2017 和 VOC 2007 数据集，组装了一套挑战集，以促进通用对象检索模型的训练和评估。综合评估表明，我们的无监督 MS-UGCML 方法在对象级别和图像级别的 mAP 提升了 6.69% 和 10.03%。

无监督的混合尺度群组协作度量学习对于普通物体检索的应用

Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for  General Object Retrieval

Deep metric learning is an important area due to its applicability to many
domains such as image retrieval and person re-identification. The main drawback
of such models is the necessity for labeled data. In this work, we propose to
generate pseudo-labels for deep metric learning directly from clustering
assignment and we introduce unsupervised deep metric learning (UDML)
regularized by a self-supervision (SS) task. In particular, we propose to
regularize the training process by predicting image rotations. Our method
(UDML-SS) jointly learns discriminative embeddings, unsupervised clustering
assignments of the embeddings, as well as a self-supervised pretext task.
UDML-SS iteratively cluster embeddings using traditional clustering algorithm
(e.g., k-means), and sampling training pairs based on the cluster assignment
for metric learning, while optimizing self-supervised pretext task in a
multi-task fashion. The role of self-supervision is to stabilize the training
process and encourages the model to learn meaningful feature representations
that are not distorted due to unreliable clustering assignments. The proposed
method performs well on standard benchmarks for metric learning, where it
outperforms current state-of-the-art approaches by a large margin and it also
shows competitive performance with various metric learning loss functions.

本文提出了一种基于无监督聚类算法的伪标签来生成深度度量学习中的标签，使用自监督 (SS) 任务的无监督深度度量学习 (UDML)，并通过预测图像旋转来规范化训练过程，从而实现了聚类嵌入、自监督预期任务和一致性优化的联合学习。