  Counting repetitive actions in long untrimmed videos is a challenging task
that has many applications such as rehabilitation. State-of-the-art methods
predict action counts by first generating a temporal self-similarity matrix
(TSM) from the sampled frames and then feeding the matrix to a predictor
network. The self-similarity matrix, however, is not an optimal input to a
network since it discards too much information from the frame-wise embeddings.
We thus rethink how a TSM can be utilized for counting repetitive actions and
propose a framework that learns embeddings and predicts action start
probabilities at full temporal resolution. The number of repeated actions is
then inferred from the action start probabilities. In contrast to current
approaches that have the TSM as an intermediate representation, we propose a
novel loss based on a generated reference TSM, which enforces that the
self-similarity of the learned frame-wise embeddings is consistent with the
self-similarity of repeated actions. The proposed framework achieves
state-of-the-art results on three datasets, i.e., RepCount, UCFRep, and
Countix.
