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Attempt to fully discover the temporal diversity and chronologicalcharacteristics for self-supervised video representation learning, this worktakes advantage of the temporal dependencies within videos and further proposesa novel self-supervised method named Temporal Contrastive Graph Learning(TCGL). In contrast to the existing methods that ignore modeling elaboratetemporal dependencies, our TCGL roots in a hybrid graph contrastive learningstrategy to jointly regard the inter-snippet and intra-snippet temporaldependencies as self-supervision signals for temporal representation learning.To model multi-scale temporal dependencies, our TCGL integrates the priorknowledge about the frame and snippet orders into graph structures, i.e., theintra-/inter- snippet temporal contrastive graphs. By randomly removing edgesand masking nodes of the intra-snippet graphs or inter-snippet graphs, our TCGLcan generate different correlated graph views. Then, specific contrastivelearning modules are designed to maximize the agreement between nodes indifferent views. To adaptively learn the global context representation andrecalibrate the channel-wise features, we introduce an adaptive video snippetorder prediction module, which leverages the relational knowledge among videosnippets to predict the actual snippet orders. Experimental results demonstratethe superiority of our TCGL over the state-of-the-art methods on large-scaleaction recognition and video retrieval benchmarks.

Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval