HalluciNet-<i>ing</i> Spatiotemporal Representations Using a 2D-CNN

• Main Authors: Paritosh Parmar, Brendan Morris
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Signals
• Subjects: action recognition; scene recognition; action quality assessment; activity recognition; deep learning; computer vision
• Online Access: https://www.mdpi.com/2624-6120/2/3/37

Spatiotemporal representations learned using 3D convolutional neural networks (CNN) are currently used in state-of-the-art approaches for action-related tasks. However, 3D-CNN are notorious for being memory and compute resource intensive as compared with more simple 2D-CNN architectures. We propose to hallucinate spatiotemporal representations from a 3D-CNN teacher with a 2D-CNN student. By requiring the 2D-CNN to predict the future and intuit upcoming activity, it is encouraged to gain a deeper understanding of actions and how they evolve. The hallucination task is treated as an auxiliary task, which can be used with any other action-related task in a multitask learning setting. Thorough experimental evaluation, it is shown that the hallucination task indeed helps improve performance on action recognition, action quality assessment, and dynamic scene recognition tasks. From a practical standpoint, being able to hallucinate spatiotemporal representations without an actual 3D-CNN can enable deployment in resource-constrained scenarios, such as with limited computing power and/or lower bandwidth. We also observed that our hallucination task has utility not only during the training phase, but also during the pre-training phase.