  The complexity of state-of-the-art Transformer-based models for
skeleton-based action recognition poses significant challenges in terms of
computational efficiency and resource utilization. In this paper, we explore
the application of Singular Value Decomposition (SVD) to effectively reduce the
model sizes of these pre-trained models, aiming to minimize their resource
consumption while preserving accuracy. Our method, LORTSAR (LOw-Rank
Transformer for Skeleton-based Action Recognition), also includes a fine-tuning
step to compensate for any potential accuracy degradation caused by model
compression, and is applied to two leading Transformer-based models,
"Hyperformer" and "STEP-CATFormer". Experimental results on the "NTU RGB+D" and
"NTU RGB+D 120" datasets show that our method can reduce the number of model
parameters substantially with negligible degradation or even performance
increase in recognition accuracy. This confirms that SVD combined with
post-compression fine-tuning can boost model efficiency, paving the way for
more sustainable, lightweight, and high-performance technologies in human
action recognition.
