Skeleton-based action recognition is vital for comprehending human-centric
videos and has applications in diverse domains. One of the challenges of
skeleton-based action recognition is dealing with low-quality data, such as
skeletons that have missing or inaccurate joints. This paper addresses the
issue of enhancing action recognition using low-quality skeletons through a
general knowledge distillation framework. The proposed framework employs a
teacher-student model setup, where a teacher model trained on high-quality
skeletons guides the learning of a student model that handles low-quality
skeletons. To bridge the gap between heterogeneous high-quality and lowquality
skeletons, we present a novel part-based skeleton matching strategy, which
exploits shared body parts to facilitate local action pattern learning. An
action-specific part matrix is developed to emphasize critical parts for
different actions, enabling the student model to distill discriminative
part-level knowledge. A novel part-level multi-sample contrastive loss achieves
knowledge transfer from multiple high-quality skeletons to low-quality ones,
which enables the proposed knowledge distillation framework to include training
low-quality skeletons that lack corresponding high-quality matches.
Comprehensive experiments conducted on the NTU-RGB+D, Penn Action, and SYSU 3D
HOI datasets demonstrate the effectiveness of the proposed knowledge
distillation framework.

为解决低质量骨架动作识别问题，本文提出了一个通用的知识蒸馏框架，利用师生模型结构和基于部分的骨架匹配策略，通过多样本对比损失实现从高质量到低质量骨架的知识迁移，进而训练出可以处理低质量骨架的学生模型。实验证明了该知识蒸馏框架的有效性。

通过部分知识蒸馏提高低质量骨架数据的动作识别能力

Enhancing Action Recognition from Low-Quality Skeleton Data via  Part-Level Knowledge Distillation

Domain adaptation deals with training models using large scale labeled data
from a specific source domain and then adapting the knowledge to certain target
domains that have few or no labels. Many prior works learn domain agnostic
feature representations for this purpose using a global distribution alignment
objective which does not take into account the finer class specific structure
in the source and target domains. We address this issue in our work and propose
an instance affinity based criterion for source to target transfer during
adaptation, called ILA-DA. We first propose a reliable and efficient method to
extract similar and dissimilar samples across source and target, and utilize a
multi-sample contrastive loss to drive the domain alignment process. ILA-DA
simultaneously accounts for intra-class clustering as well as inter-class
separation among the categories, resulting in less noisy classifier boundaries,
improved transferability and increased accuracy. We verify the effectiveness of
ILA-DA by observing consistent improvements in accuracy over popular domain
adaptation approaches on a variety of benchmark datasets and provide insights
into the proposed alignment approach. Code will be made publicly available at
this https URL

本研究提出一种基于实例相似度和分类内聚性和分类分离性的目标域适应策略 (ILA-DA)，其使用多样本对比损失函数来驱动领域对齐过程，在多种基准数据集上实现明显的准确率提高。