In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks. To this end, we present the novel Multi-Fiber architecture that slices a complex neural network into an ensemble of lightweight networks or fibers that run through the network. To facilitate information flow between fibers we further incorporate multiplexer modules and end up with an architecture that reduces the computational cost of 3D networks by an order of magnitude, while increasing recognition performance at the same time. Extensive experimental results show that our multi-fiber architecture significantly boosts the efficiency of existing convolution networks for both image and video recognition tasks, achieving state-of-the-art performance on UCF-101, HMDB-51 and Kinetics datasets. Our proposed model requires over 9x and 13x less computations than the I3D and R(2+1)D models, respectively, yet providing higher accuracy.

本文介绍了一种名为Multi-Fiber的新型神经网络架构，它将一个复杂的神经网络切分成多个轻量级网络，以显著降低3D网络的计算成本，同时保持与现有2D网络相当的识别准确性，可以显著提高视频和图像识别任务的效率，并在UCF-101，HMDB-51和Kinetics数据集上达到了最先进的性能水平。

视频识别的多光纤网络