  Action detection is an essential and challenging task, especially for densely
labelled datasets of untrimmed videos. There are many real-world challenges in
those datasets, such as composite action, co-occurring action, and high
temporal variation of instance duration. For handling these challenges, we
propose to explore both the class and temporal relations of detected actions.
In this work, we introduce an end-to-end network: Class-Temporal Relational
Network (CTRN). It contains three key components: (1) The Representation
Transform Module filters the class-specific features from the mixed
representations to build graph-structured data. (2) The Class-Temporal Module
models the class and temporal relations in a sequential manner. (3)
G-classifier leverages the privileged knowledge of the snippet-wise
co-occurring action pairs to further improve the co-occurring action detection.
We evaluate CTRN on three challenging densely labelled datasets and achieve
state-of-the-art performance, reflecting the effectiveness and robustness of
our method.
