September 9, 2018
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 9× and 13× less computations than the I3D [1] and R(2+1)D [2] models, respectively, yet providing higher accuracy.
Research Topics
November 10, 2022
Unnat Jain, Abhinav Gupta, Himangi Mittal, Pedro Morgado
November 10, 2022
November 06, 2022
Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan
November 06, 2022
October 25, 2022
Mustafa Mukadam, Austin Wang, Brandon Amos, Daniel DeTone, Jing Dong, Joe Ortiz, Luis Pineda, Maurizio Monge, Ricky Chen, Shobha Venkataraman, Stuart Anderson, Taosha Fan, Paloma Sodhi
October 25, 2022
October 22, 2022
Naila Murray, Lei Wang, Piotr Koniusz, Shan Zhang
October 22, 2022
April 30, 2018
Yedid Hoshen, Lior Wolf
April 30, 2018
December 11, 2019
Eliya Nachmani, Lior Wolf
December 11, 2019
April 30, 2018
Yedid Hoshen, Lior Wolf
April 30, 2018
November 01, 2018
Yedid Hoshen, Lior Wolf
November 01, 2018
Foundational models
Latest news
Foundational models