  Attempt to fully discover the temporal diversity and chronological
characteristics for self-supervised video representation learning, this work
takes advantage of the temporal dependencies within videos and further proposes
a novel self-supervised method named Temporal Contrastive Graph Learning
(TCGL). In contrast to the existing methods that ignore modeling elaborate
temporal dependencies, our TCGL roots in a hybrid graph contrastive learning
strategy to jointly regard the inter-snippet and intra-snippet temporal
dependencies as self-supervision signals for temporal representation learning.
To model multi-scale temporal dependencies, our TCGL integrates the prior
knowledge about the frame and snippet orders into graph structures, i.e., the
intra-/inter- snippet temporal contrastive graphs. By randomly removing edges
and masking nodes of the intra-snippet graphs or inter-snippet graphs, our TCGL
can generate different correlated graph views. Then, specific contrastive
learning modules are designed to maximize the agreement between nodes in
different views. To adaptively learn the global context representation and
recalibrate the channel-wise features, we introduce an adaptive video snippet
order prediction module, which leverages the relational knowledge among video
snippets to predict the actual snippet orders. Experimental results demonstrate
the superiority of our TCGL over the state-of-the-art methods on large-scale
action recognition and video retrieval benchmarks.
