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Proceedings Paper

Efficient training and inference in highly temporal activity recognition
Author(s): Masoud Charkhabi; Nivedita Rahurkar
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Paper Abstract

High-performance Activity Recognition models from video data are difficult to train and deploy efficiently. We measure efficiency in performance, model size, and run-time; during training and inference. Researchers have demonstrated that 3D convolutions capture the space-time dynamics well [13]. The challenge is that 3D convolutions are computationally intensive. [8] Propose the Temporal Shift Module (TSM) for train-efficiency, and [5] proposes DeepCompression for inference-efficiency. TSM is a simple yet effective way to gain near 3D convolution performance at 2D convolution computation cost. We apply these efficiency techniques to a newly labeled activity recognition data set through transfer learning. Our labeling strategy is meant to create highly temporal activity. We benchmark against a 2D ResNet50 backbone trained on individual frames, and a multilayer 3DCNN on multi-frame short videos. Our contributions are: 1. A new highly temporal activity recognition dataset based on egoHands [1]. 2. results that show a 3D backbone on videos outperforms a 2D one on frames. 3. With TSM we achieve 5x train efficiency in run-time with negligible performance loss. 4. With Quantization alone we achieve 10x inference efficiency in model size with negligible performance loss.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211N (27 November 2019); doi: 10.1117/12.2550596
Show Author Affiliations
Masoud Charkhabi, Stanford Univ. (United States)
Nivedita Rahurkar, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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