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In this work we propose a multi-task spatio-temporal network, called SUSiNet,that can jointly tackle the spatio-temporal problems of saliency estimation,action recognition and video summarization. Our approach employs a singlenetwork that is jointly end-to-end trained for all tasks with multiple anddiverse datasets related to the exploring tasks. The proposed network uses aunified architecture that includes global and task specific layer and producesmultiple output types, i.e., saliency maps or classification labels, byemploying the same video input. Moreover, one additional contribution is thatthe proposed network can be deeply supervised through an attention module thatis related to human attention as it is expressed by eye-tracking data. From theextensive evaluation, on seven different datasets, we have observed that themulti-task network performs as well as the state-of-the-art single-task methods(or in some cases better), while it requires less computational budget thanhaving one independent network per each task.

SUSiNet: See, Understand and Summarize it