  Fine-grained temporal action parsing is important in many applications, such
as daily activity understanding, human motion analysis, surgical robotics and
others requiring subtle and precise operations in a long-term period. In this
paper we propose a novel bilinear pooling operation, which is used in
intermediate layers of a temporal convolutional encoder-decoder net. In
contrast to other work, our proposed bilinear pooling is learnable and hence
can capture more complex local statistics than the conventional counterpart. In
addition, we introduce exact lower-dimension representations of our bilinear
forms, so that the dimensionality is reduced with neither information loss nor
extra computation. We perform intensive experiments to quantitatively analyze
our model and show the superior performances to other state-of-the-art work on
various datasets.
