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Recent advances in computer vision have made it possible to automaticallyassess from videos the manipulation skills of humans in performing a task,which breeds many important applications in domains such as healthrehabilitation and manufacturing. Previous methods of video-based skillassessment did not consider the attention mechanism humans use in assessingvideos, limiting their performance as only a small part of video regions isinformative for skill assessment. Our motivation here is to estimate attentionin videos that helps to focus on critically important video regions for betterskill assessment. In particular, we propose a novel RNN-based spatial attentionmodel that considers accumulated attention state from previous frames as wellas high-level knowledge about the progress of an undergoing task. We evaluateour approach on a newly collected dataset of infant grasping task and fourexisting datasets of hand manipulation tasks. Experiment results demonstratethat state-of-the-art performance can be achieved by considering attention inautomatic skill assessment.

Manipulation-skill Assessment from Videos with Spatial Attention Network