text: 198,99,1425057742583181314,7773042,Yasser Souri,"Happy to announce our new paper: ""FIFA: Fast Inference Approximation for Action Segmentation"". Paper: <LINK> Animation: <LINK> We propose an approximate approach to perform inference for action segmentation using gradient-descent instead of dynamic programming. Using gradient based optimization results in a fast and ""any-time"" algorithm where one can stop the optimization after any number of steps. <LINK> FIFA can be used in combination with any action segmentation approach that estimates framewise probabilities. We use FIFA in combination with MuCon, CDFL, MS-TCN and MS-TCN++ in both fully supervised and weakly supervised settings. We report state-of-the-art or comparable results on the Breakfast and Hollywood extended dataset. FIFA achieves this accuracy while being 5-12 times faster than dynamic programming based exact inference algorithms.",https://arxiv.org/abs/2108.03894,"We introduce FIFA, a fast approximate inference method for action segmentation and alignment. Unlike previous approaches, FIFA does not rely on expensive dynamic programming for inference. Instead, it uses an approximate differentiable energy function that can be minimized using gradient-descent. FIFA is a general approach that can replace exact inference improving its speed by more than 5 times while maintaining its performance. FIFA is an anytime inference algorithm that provides a better speed vs. accuracy trade-off compared to exact inference. We apply FIFA on top of state-of-the-art approaches for weakly supervised action segmentation and alignment as well as fully supervised action segmentation. FIFA achieves state-of-the-art results on most metrics on two action segmentation datasets. ",FIFA: Fast Inference Approximation for Action Segmentation,4,"['Happy to announce our new paper: ""FIFA: Fast Inference Approximation for Action Segmentation"".\n\nPaper: <LINK>\nAnimation: <LINK>', 'We propose an approximate approach to perform inference for action segmentation using gradient-descent instead of dynamic programming.\nUsing gradient based optimization results in a fast and ""any-time"" algorithm where one can stop the optimization after any number of steps. https://t.co/VZev9DZmh3', 'FIFA can be used in combination with any action segmentation approach that estimates framewise probabilities.\nWe use FIFA in combination with MuCon, CDFL, MS-TCN and MS-TCN++ in both fully supervised and weakly supervised settings.', 'We report state-of-the-art or comparable results on the Breakfast and Hollywood extended dataset.\nFIFA achieves this accuracy while being 5-12 times faster than dynamic programming based exact inference algorithms.']",21,08,855