  While conventional methods for sequential learning focus on interaction
between consecutive inputs, we suggest a new method which captures composite
semantic flows with variable-length dependencies. In addition, the semantic
structures within given sequential data can be interpreted by visualizing
temporal dependencies learned from the method. The proposed method, called
Temporal Dependency Network (TDN), represents a video as a temporal graph whose
node represents a frame of the video and whose edge represents the temporal
dependency between two frames of a variable distance. The temporal dependency
structure of semantic is discovered by learning parameterized kernels of graph
convolutional methods. We evaluate the proposed method on the large-scale video
dataset, Youtube-8M. By visualizing the temporal dependency structures as
experimental results, we show that the suggested method can find the temporal
dependency structures of video semantic.
