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

• URL: http://arxiv.org/abs/2006.08247v2
• Date: Tue, 23 Jun 2020 14:06:36 GMT
• Title: Learn to cycle: Time-consistent feature discovery for action recognition
• Authors: Alexandros Stergiou and Ronald Poppe
• Abstract summary: Generalizing over temporal variations is a prerequisite for effective action recognition in videos. We introduce Squeeze Re Temporal Gates (SRTG), an approach that favors temporal activations with potential variations. We show consistent improvement when using SRTPG blocks, with only a minimal increase in the number of GFLOs.
• Score: 83.43682368129072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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