  In temporal action localization methods, temporal downsampling operations are
widely used to extract proposal features, but they often lead to the aliasing
problem, due to lacking consideration of sampling rates. This paper aims to
verify the existence of aliasing in TAL methods and investigate utilizing low
pass filters to solve this problem by inhibiting the high-frequency band.
However, the high-frequency band usually contains large amounts of specific
information, which is important for model inference. Therefore, it is necessary
to make a tradeoff between anti-aliasing and reserving high-frequency
information. To acquire optimal performance, this paper learns different cutoff
frequencies for different instances dynamically. This design can be plugged
into most existing temporal modeling programs requiring only one additional
cutoff frequency parameter. Integrating low pass filters to the downsampling
operations significantly improves the detection performance and achieves
comparable results on THUMOS'14, ActivityNet~1.3, and Charades datasets.
Experiments demonstrate that anti-aliasing with low pass filters in TAL is
advantageous and efficient.
