vCLIMB: A Novel Video Class Incremental Learning Benchmark

Andres Villa, Kumail Alhamoud, Victor Escorcia, Fabian Caba Heilbron, Juan Leon Alcazar, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


Continual learning (CL) is under-explored in the video domain. The few existing works contain splits with imbalanced class distributions over the tasks, or study the problem in unsuitable datasets. We introduce vCLIMB, a novel video continual learning benchmark. vCLIMB is a standardized test-bed to analyze catastrophic forgetting of deep models in video continual learning. In contrast to previous work, we focus on class incremental continual learning with models trained on a sequence of disjoint tasks, and distribute the number of classes uniformly across the tasks. We perform in-depth evaluations of existing CL methods in vCLIMB, and observe two unique challenges in video data. The selection of instances to store in episodic memory is performed at the frame level. Second, untrimmed training data influences the effectiveness of frame sampling strategies. We address these two challenges by proposing a temporal consistency regularization that can be applied on top of memory-based continual learning methods. Our approach significantly improves the baseline, by up to 24% on the untrimmed continual learning task. The code of our benchmark can be found at:

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)9781665469463
StatePublished - 2022
Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States
Duration: Jun 19 2022Jun 24 2022

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
Country/TerritoryUnited States
CityNew Orleans

Bibliographical note

Funding Information:
Acknowledgments. This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2021-4648. Authors also thank Centro Nacional de Inteligencia Artificial CENIA, FB210017, BASAL, ANID.

Publisher Copyright:
© 2022 IEEE.


  • Action and event recognition
  • Datasets and evaluation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'vCLIMB: A Novel Video Class Incremental Learning Benchmark'. Together they form a unique fingerprint.

Cite this