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arXiv · 2006.08247

Learn to cycle: Time-consistent feature discovery for action recognition

Alexandros Stergiou·Ronald Poppe
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Abstract

Generalizing over temporal variations is a prerequisite for effective action recognition in videos. Despite significant advances in deep neural networks, it remains a challenge to focus on short-term discriminative motions in relation to the overall performance of an action. We address this challenge by allowing some flexibility in discovering relevant spatio-temporal features. We introduce Squeeze and Recursion Temporal Gates (SRTG), an approach that favors inputs with similar activations with potential temporal variations. We implement this idea with a novel CNN block that uses an LSTM to encapsulate feature dynamics, in conjunction with a temporal gate that is responsible for evaluating the consistency of the discovered dynamics and the modeled features. We show consistent improvement when using SRTG blocks, with only a minimal increase in the number of GFLOPs. On Kinetics-700, we perform on par with current state-of-the-art models, and outperform these on HACS, Moments in Time, UCF-101 and HMDB-51.

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Alexandros Stergiou, Ronald Poppe. 2020-06-15. Learn to cycle: Time-consistent feature discovery for action recognition. https://doi.org/10.1016/j.patrec.2020.11.012

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