3 research outputs found
Localizing the Common Action Among a Few Videos
This paper strives to localize the temporal extent of an action in a long
untrimmed video. Where existing work leverages many examples with their start,
their ending, and/or the class of the action during training time, we propose
few-shot common action localization. The start and end of an action in a long
untrimmed video is determined based on just a hand-full of trimmed video
examples containing the same action, without knowing their common class label.
To address this task, we introduce a new 3D convolutional network architecture
able to align representations from the support videos with the relevant query
video segments. The network contains: (\textit{i}) a mutual enhancement module
to simultaneously complement the representation of the few trimmed support
videos and the untrimmed query video; (\textit{ii}) a progressive alignment
module that iteratively fuses the support videos into the query branch; and
(\textit{iii}) a pairwise matching module to weigh the importance of different
support videos. Evaluation of few-shot common action localization in untrimmed
videos containing a single or multiple action instances demonstrates the
effectiveness and general applicability of our proposal.Comment: ECCV 202