Hazel Doughty, Walterio Mayol-Cuevas, Dima Damen
In CVPR2018, we presented a method for assessing skill of performance from video, applicable to a variety of tasks, ranging from surgery to drawing
and rolling pizza dough. We formulate the problem as pairwise ( |
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In Dec 2018, we present a new model to determine relative skill from long videos, through learnable temporal attention modules. We propose to train rank-specific temporal attention modules, learned with only video-level supervision, using a novel rank-aware loss function. In addition to attending to task-relevant video parts, our proposed loss jointly trains two attention modules to separately attend to video parts which are indicative of higher (pros) and lower (cons) skills. |
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PublicationsHazel Doughty, Walterio Mayol-Cuevas, Dima Damen (2018). The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos. Arxiv. arxiv Hazel Doughty, Dima Damen and Walterio Mayol-Cuevas (2018). Who's Better, Who's Best: Skill Determination in Video using Deep Ranking. CVPR. PDF arXiv Datasets
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