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Temporal receptive fields of models play an important role in actionsegmentation. Large receptive fields facilitate the long-term relations amongvideo clips while small receptive fields help capture the local details.Existing methods construct models with hand-designed receptive fields inlayers. Can we effectively search for receptive field combinations to replacehand-designed patterns? To answer this question, we propose to find betterreceptive field combinations through a global-to-local search scheme. Oursearch scheme exploits both global search to find the coarse combinations andlocal search to get the refined receptive field combination patterns further.The global search finds possible coarse combinations other than human-designedpatterns. On top of the global search, we propose an expectation guidediterative local search scheme to refine combinations effectively. Ourglobal-to-local search can be plugged into existing action segmentation methodsto achieve state-of-the-art performance.

Global2Local: Efficient Structure Search for Video Action Segmentation