text:   Segmentation",7,"['#CVPR2020 Learning To Temporally Segment Untrimmed Videos from Set of Actions:\n""SCT: Set Constrained Temporal Transformer for Set Supervised Action Segmentation""\nA new paper by me and my supervisor Juergen Gall.\n<LINK> <LINK>', 'In a set supervised action segmentation problem, for each training video, only the list of actions is given that\noccur in the video, but not when, how often, and in which order they occur.', 'Our network divides a video into smaller temporal regions. For each region, the network estimates its length and the corresponding action label. Then the upsampling module uses the lengths and the action probabilities of all regions to estimate the framewise probabilities.', 'Although it is possible to do the upsampling by linear interpolation this operation is not differentiable w.r.t predicted lengths. Therefore, we use our novel upsampling module which is differentiable w.r.t. predicted lengths.', 'Since we do not know the ground-truth lengths and orders but only the set of present actions, we cannot directly use the predicted frame-wise probabilities of the network.', 'Therefore,  we introduce a novel temporal transformation method that transforms a temporal sequence to the set of action probabilities w.r.t predicted lengths and temporal locations of actions. Using the set of action probabilities we can use the GT to train the model end-to-end.', 'We will release the source code as soon as possible.\nThanks @yassersouri for his valuable comments on our work.']",20,03,1479