SVW-UCF Dataset for Video Domain Adaptation

Artjoms Gorpincenko, Michal Mackiewicz

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

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Abstract

Unsupervised video domain adaptation (DA) has recently seen a lot of success, achieving almost if not perfect results on the majority of various benchmark datasets. Therefore, the next natural step for the field is to come up with new, more challenging problems that call for creative solutions. By combining two well known sets of data - SVW and UCF, we propose a large-scale video domain adaptation dataset that is not only larger in terms of samples and average video length, but also presents additional obstacles, such as orientation and intra-class variations, differences in resolution, and greater domain discrepancy, both in terms of content and capturing conditions. We perform an accuracy gap comparison which shows that both SVW→UCF and UCF→SVW are empirically more difficult to solve than existing adaptation paths. Finally, we evaluate two state of the art video DA algorithms on the dataset to present the benchmark results and provide a discussion on the properties which create the most confusion for modern video domain adaptation methods.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Image Processing and Vision Engineering (IMPROVE 2021)
EditorsFrancisco Imai, Cosimo Distante, Sebastiano Battiato
Pages107-111
Number of pages5
ISBN (Electronic)9789897585111
DOIs
Publication statusPublished - Apr 2021

Publication series

NameProceedings of the International Conference on Image Processing and Vision Engineering, IMPROVE 2021

Keywords

  • Dataset
  • Deep Learning
  • Domain Adaptation
  • Video

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