Deep clustering, which learns representation and semantic clustering without
labels information, poses a great challenge for deep learning-based approaches.
Despite significant progress in recent years, most existing methods focus on
uniformly distributed datasets, significantly limiting the practical
applicability of their methods. In this paper, we propose a more practical
problem setting named deep imbalanced clustering, where the underlying classes
exhibit an imbalance distribution. To address this challenge, we introduce a
novel optimal transport-based pseudo-label learning framework. Our framework
formulates pseudo-label generation as a Semantic-regularized Progressive
Partial Optimal Transport (SP$^2$OT) problem, which progressively transports
each sample to imbalanced clusters under several prior distribution and
semantic relation constraints, thus generating high-quality and imbalance-aware
pseudo-labels. To solve SP$^2$OT, we develop a Majorization-Minimization-based
optimization algorithm. To be more precise, we employ the strategy of
majorization to reformulate the SP$^2$OT problem into a Progressive Partial
Optimal Transport problem, which can be transformed into an unbalanced optimal
transport problem with augmented constraints and can be solved efficiently by a
fast matrix scaling algorithm. Experiments on various datasets, including a
human-curated long-tailed CIFAR100, challenging ImageNet-R, and large-scale
subsets of fine-grained iNaturalist2018 datasets, demonstrate the superiority
of our method.

我们提出了一个基于最优传输的伪标签学习框架来解决深度不平衡聚类问题，该框架通过逐步将样本转移到不平衡的聚类中生成高质量且注意不平衡性的伪标签。

SP$^2$OT：语义规范化的逐步偏差最优传输用于不平衡聚类

SP$^2$OT: Semantic-Regularized Progressive Partial Optimal Transport for  Imbalanced Clustering

We address the problem of unsupervised semantic segmentation of outdoor LiDAR
point clouds in diverse traffic scenarios. The key idea is to leverage the
spatiotemporal nature of a dynamic point cloud sequence and introduce
drastically stronger augmentation by establishing spatiotemporal
correspondences across multiple frames. We dovetail clustering and pseudo-label
learning in this work. Essentially, we alternate between clustering points into
semantic groups and optimizing models using point-wise pseudo-spatiotemporal
labels with a simple learning objective. Therefore, our method can learn
discriminative features in an unsupervised learning fashion. We show promising
segmentation performance on Semantic-KITTI, SemanticPOSS, and FLORIDA benchmark
datasets covering scenarios in autonomous vehicle and intersection
infrastructure, which is competitive when compared against many existing fully
supervised learning methods. This general framework can lead to a unified
representation learning approach for LiDAR point clouds incorporating domain
knowledge.

我们解决了在各种交通场景下，对室外 LiDAR 点云进行无监督语义分割的问题，通过建立多帧之间的时空对应关系，将点云序列的时空特征引入强化扩充，融合聚类和伪标签学习，以无监督的学习方式学习辨别特征，展现出在自动驾驶车辆和交叉口基础设施方面的分割性能在 Semantic-KITTI，SemanticPOSS 和 FLORIDA 基准数据集上表现得很有竞争力，该通用框架可以为 LiDAR 点云的统一表示学习方法融入领域知识。