text: 337,72,1115604309993857026,7773042,Yasser Souri,"""Weakly Supervised Action Segmentation Using Mutual Consistency"" A new paper by me, @MohsenFyz and our advisor, Juergen Gall. <LINK> We propose a new approach for action segmentation using transcripts as the weak supervision. <LINK> Our network produces two redundant representations for action segmentation (frame-level and segment-level representations) and during training requires them to match each other using a new loss function that we term MuCon. We also have a transcript prediction loss (as in seq2seq) The approach is fast during training: doesn't require Viterbi or pseudo ground truth generation like many of the previous works. It is also differentiable, so no heuristic updates of parameters, only SGD. At test time we predict both representations and fuse them for smoothness. Unlike current state-of-the-art methods that are truly only able to perform action alignment at test time and mimic action segmentation by choosing one of the training transcripts, we are able to perform action segmentation at test time. Our work is currently under review. Code is ""coming soon""! We would really appreciate your feedback. souri@iai.uni-bonn.de or tweet at me.",https://arxiv.org/abs/1904.03116,"Action segmentation is the task of predicting the actions for each frame of a video. As obtaining the full annotation of videos for action segmentation is expensive, weakly supervised approaches that can learn only from transcripts are appealing. In this paper, we propose a novel end-to-end approach for weakly supervised action segmentation based on a two-branch neural network. The two branches of our network predict two redundant but different representations for action segmentation and we propose a novel mutual consistency (MuCon) loss that enforces the consistency of the two redundant representations. Using the MuCon loss together with a loss for transcript prediction, our proposed approach achieves the accuracy of state-of-the-art approaches while being $14$ times faster to train and $20$ times faster during inference. The MuCon loss proves beneficial even in the fully supervised setting. ",Fast Weakly Supervised Action Segmentation Using Mutual Consistency,5,"['""Weakly Supervised Action Segmentation Using Mutual Consistency""\nA new paper by me, @MohsenFyz and our advisor, Juergen Gall.\n<LINK>\nWe propose a new approach for action segmentation using transcripts as the weak supervision. <LINK>', 'Our network produces two redundant representations for action segmentation (frame-level and segment-level representations) and during training requires them to match each other using a new loss function that we term MuCon.\nWe also have a transcript prediction loss (as in seq2seq)', ""The approach is fast during training: doesn't require Viterbi or pseudo ground truth generation like many of the previous works.\nIt is also differentiable, so no heuristic updates of parameters, only SGD.\nAt test time we predict both representations and fuse them for smoothness."", 'Unlike current state-of-the-art methods that are truly only able to perform action alignment at test time and mimic action segmentation by choosing one of the training transcripts, we are able to perform action segmentation at test time.', 'Our work is currently under review. Code is ""coming soon""!\nWe would really appreciate your feedback.\nsouri@iai.uni-bonn.de or tweet at me.']",19,04,1170