Numerous point-cloud understanding techniques focus on whole entities and
have succeeded in obtaining satisfactory results and limited sparsity
tolerance. However, these methods are generally sensitive to incomplete point
clouds that are scanned with flaws or large gaps. To address this issue, in
this paper, we propose an end-to-end architecture that compensates for and
identifies partial point clouds on the fly. First, we propose a cascaded
solution that integrates both the upstream and downstream networks
simultaneously, allowing the task-oriented downstream to identify the points
generated by the completion-oriented upstream. These two streams complement
each other, resulting in improved performance for both completion and
downstream-dependent tasks. Second, to explicitly understand the predicted
points' pattern, we introduce hierarchical self-distillation (HSD), which can
be applied to arbitrary hierarchy-based point cloud methods. HSD ensures that
the deepest classifier with a larger perceptual field and longer code length
provides additional regularization to intermediate ones rather than simply
aggregating the multi-scale features, and therefore maximizing the mutual
information between a teacher and students. We show the advantage of the
self-distillation process in the hyperspaces based on the information
bottleneck principle. On the classification task, our proposed method performs
competitively on the synthetic dataset and achieves superior results on the
challenging real-world benchmark when compared to the state-of-the-art models.
Additional experiments also demonstrate the superior performance and generality
of our framework on the part segmentation task.

本文提出了一种端到端的架构，用于处理和识别部分点云，具有层级自蒸馏和在部分分割任务上具有卓越性能的优势。

分层自蒸馏下的散点云理解的联合学习

Joint Learning for Scattered Point Cloud Understanding with Hierarchical  Self-Distillation

Remote photoplethysmography (rPPG) is a noninvasive technique that aims to
capture subtle variations in facial pixels caused by changes in blood volume
resulting from cardiac activities. Most existing unsupervised methods for rPPG
tasks focus on the contrastive learning between samples while neglecting the
inherent self-similar prior in physiological signals. In this paper, we propose
a Self-Similarity Prior Distillation (SSPD) framework for unsupervised rPPG
estimation, which capitalizes on the intrinsic self-similarity of cardiac
activities. Specifically, we first introduce a physical-prior embedded
augmentation technique to mitigate the effect of various types of noise. Then,
we tailor a self-similarity-aware network to extract more reliable self-similar
physiological features. Finally, we develop a hierarchical self-distillation
paradigm to assist the network in disentangling self-similar physiological
patterns from facial videos. Comprehensive experiments demonstrate that the
unsupervised SSPD framework achieves comparable or even superior performance
compared to the state-of-the-art supervised methods. Meanwhile, SSPD maintains
the lowest inference time and computation cost among end-to-end models. The
source codes are available at this https URL

我们提出了一种基于自相似性先验蒸馏 (SSPD) 框架的无监督远程光电容积描记术 (rPPG) 估计方法，通过利用心脏活动的内在自相似性，采用物理先验嵌入增强技术抑制噪音，利用自相似性感知网络提取更可靠的自相似生理特征，以及开发分层自蒸馏范式来帮助网络从面部视频中解开自相似的生理模式。全面的实验证明，无监督的 SSPD 框架在性能上相当或甚至优于最先进的有监督方法，同时 SSPD 在端到端模型中保持最低的推断时间和计算成本。