Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition

Taeoh Kim, Hyeongmin Lee, Myeong Ah Cho, Ho Seong Lee, Dong Heon Cho, Sangyoun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Deep-Learning-based video recognition has shown promising improvements along with the development of large-scale datasets and spatiotemporal network architectures. In image recognition, learning spatially invariant features is a key factor in improving recognition performance and robustness. Data augmentation based on visual inductive priors, such as cropping, flipping, rotating, or photometric jittering, is a representative approach to achieve these features. Recent state-of-the-art recognition solutions have relied on modern data augmentation strategies that exploit a mixture of augmentation operations. In this study, we extend these strategies to the temporal dimension for videos to learn temporally invariant or temporally localizable features to cover temporal perturbations or complex actions in videos. Based on our novel temporal data augmentation algorithms, video recognition performances are improved using only a limited amount of training data compared to the spatial-only data augmentation algorithms, including the 1st Visual Inductive Priors (VIPriors) for data-efficient action recognition challenge. Furthermore, learned features are temporally localizable that cannot be achieved using spatial augmentation algorithms. Our source code is available at https://github.com/taeoh-kim/temporal_data_augmentation.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 Workshops, Proceedings
EditorsAdrien Bartoli, Andrea Fusiello
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783030660956
Publication statusPublished - 2020
EventWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: 2020 Aug 232020 Aug 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12536 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom

Bibliographical note

Funding Information:
Acknowledgments. This research was supported by R&D program for Advanced Integrated-intelligence for Identification (AIID) through the National Research Foundation of KOREA (NRF) funded by Ministry of Science and ICT (NRF-2018M3E3A1057289).

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition'. Together they form a unique fingerprint.

Cite this