  Video activity localization aims at understanding the semantic content in
long untrimmed videos and retrieving actions of interest. The retrieved action
with its start and end locations can be used for highlight generation, temporal
action detection, etc. Unfortunately, learning the exact boundary location of
activities is highly challenging because temporal activities are continuous in
time, and there are often no clear-cut transitions between actions. Moreover,
the definition of the start and end of events is subjective, which may confuse
the model. To alleviate the boundary ambiguity, we propose to study the video
activity localization problem from a denoising perspective. Specifically, we
propose an encoder-decoder model named DenoiseLoc. During training, a set of
action spans is randomly generated from the ground truth with a controlled
noise scale. Then we attempt to reverse this process by boundary denoising,
allowing the localizer to predict activities with precise boundaries and
resulting in faster convergence speed. Experiments show that DenoiseLoc
advances %in several video activity understanding tasks. For example, we
observe a gain of +12.36% average mAP on QV-Highlights dataset and +1.64%
<EMAIL_ADDRESS>on THUMOS'14 dataset over the baseline. Moreover, DenoiseLoc achieves
state-of-the-art performance on TACoS and MAD datasets, but with much fewer
predictions compared to other current methods.
