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Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

Creator:

Aziere, Nicolas

Abstract:

This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...

Resource Type:

Dissertation