Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition
Kiyoon Kim (University of Edinburgh),* Shreyank N Gowda (University of Edinburgh), Oisin Mac Aodha (University of Edinburgh), Laura Sevilla-Lara (University of Edinburgh)
The 33rd British Machine Vision Conference
Abstract
We address the problem of capturing temporal information for video classification in 2D networks, without increasing their computational cost. Existing approaches focus on modifying the architecture of 2D networks (e.g. by including filters in the temporal dimension to turn them into 3D networks, or using optical flow, etc.), which increases computation cost. Instead, we propose a novel sampling strategy, where we re-order the channels of the input video, to capture short-term frame-to-frame changes. We observe that without bells and whistles, the proposed sampling strategy improves performance on multiple architectures (e.g. TSN, TRN, TSM, and MVFNet) and datasets (CATER, Something-Something-V1 and V2), up to 24% over the baseline of using the standard video input. In addition, our sampling strategies do not require training from scratch and do not increase the computational cost of training and testing. Given the generality of the results and the flexibility of the approach, we hope this can be widely useful to the video understanding community. Code is available on our website: https://github.com/kiyoon/channel_sampling.Video
Citation
@inproceedings{Kim_2022_BMVC,
author = {Kiyoon Kim and Shreyank N Gowda and Oisin Mac Aodha and Laura Sevilla-Lara},
title = {Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0355.pdf}
}
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