  Learning to localize actions in long, cluttered, and untrimmed videos is a
hard task, that in the literature has typically been addressed assuming the
availability of large amounts of annotated training samples for each class --
either in a fully-supervised setting, where action boundaries are known, or in
a weakly-supervised setting, where only class labels are known for each video.
In this paper, we go a step further and show that it is possible to learn to
localize actions in untrimmed videos when a) only one/few trimmed examples of
the target action are available at test time, and b) when a large collection of
videos with only class label annotation (some trimmed and some weakly annotated
untrimmed ones) are available for training; with no overlap between the classes
used during training and testing. To do so, we propose a network that learns to
estimate Temporal Similarity Matrices (TSMs) that model a fine-grained
similarity pattern between pairs of videos (trimmed or untrimmed), and uses
them to generate Temporal Class Activation Maps (TCAMs) for seen or unseen
classes. The TCAMs serve as temporal attention mechanisms to extract
video-level representations of untrimmed videos, and to temporally localize
actions at test time. To the best of our knowledge, we are the first to propose
a weakly-supervised, one/few-shot action localization network that can be
trained in an end-to-end fashion. Experimental results on THUMOS14 and
ActivityNet1.2 datasets, show that our method achieves performance comparable
or better to state-of-the-art fully-supervised, few-shot learning methods.
