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arXiv · 1806.07383

Two Stream Self-Supervised Learning for Action Recognition

Ahmed Taha·Moustafa Meshry·Xitong Yang·Yi-Ting Chen·Larry Davis
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Abstract

We present a self-supervised approach using spatio-temporal signals between video frames for action recognition. A two-stream architecture is leveraged to tangle spatial and temporal representation learning. Our task is formulated as both a sequence verification and spatio-temporal alignment tasks. The former task requires motion temporal structure understanding while the latter couples the learned motion with the spatial representation. The self-supervised pre-trained weights effectiveness is validated on the action recognition task. Quantitative evaluation shows the self-supervised approach competence on three datasets: HMDB51, UCF101, and Honda driving dataset (HDD). Further investigations to boost performance and generalize validity are still required.

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Ahmed Taha, Moustafa Meshry, Xitong Yang, Yi-Ting Chen, Larry Davis. 2018-06-16. Two Stream Self-Supervised Learning for Action Recognition. https://arxiv.org/abs/1806.07383

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