  Recent developments in Transformers have achieved notable strides in
enhancing video comprehension. Nonetheless, the O($N^2$) computation complexity
associated with attention mechanisms presents substantial computational hurdles
when dealing with the high dimensionality of videos. This challenge becomes
particularly pronounced when striving to increase the frames per second (FPS)
to enhance the motion capturing capabilities. Such a pursuit is likely to
introduce redundancy and exacerbate the existing computational limitations. In
this paper, we initiate by showcasing the enhanced performance achieved through
an escalation in the FPS rate. Additionally, we present a novel approach,
Motion Guided Token Compression (MGTC), to empower Transformer models to
utilize a smaller yet more representative set of tokens for comprehensive video
representation. Consequently, this yields substantial reductions in
computational burden and remains seamlessly adaptable to increased FPS rates.
Specifically, we draw inspiration from video compression algorithms and
scrutinize the variance between patches in consecutive video frames across the
temporal dimension. The tokens exhibiting a disparity below a predetermined
threshold are then masked. Notably, this masking strategy effectively addresses
video redundancy while conserving essential information. Our experiments,
conducted on widely examined video recognition datasets, Kinetics-400, UCF101
and HMDB51, demonstrate that elevating the FPS rate results in a significant
top-1 accuracy score improvement of over 1.6, 1.6 and 4.0. By implementing MGTC
with the masking ratio of 25\%, we further augment accuracy by 0.1 and
simultaneously reduce computational costs by over 31\% on Kinetics-400. Even
within a fixed computational budget, higher FPS rates paired with MGTC sustain
performance gains when compared to lower FPS settings.
