U-DADA: Unsupervised Deep Action Domain Adaptation

Arshad Jamal, Vinay P. Namboodiri, Dipti Deodhare, K. S. Venkatesh

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

1 Citation (SciVal)
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The problem of domain adaptation has been extensively studied for object classification task. However, this problem has not been as well studied for recognizing actions. While, object recognition is well understood, the diverse variety of videos in action recognition make the task of addressing domain shift to be more challenging. We address this problem by proposing a new novel adaptation technique that we term as unsupervised deep action domain adaptation (U-DADA). The main concept that we propose is that of explicitly modeling density based adaptation and using them while adapting domains for recognizing actions. We show that these techniques work well both for domain adaptation through adversarial learning to obtain invariant features or explicitly reducing the domain shift between distributions. The method is shown to work well using existing benchmark datasets such as UCF50, UCF101, HMDB51 and Olympic Sports. As a pioneering effort in the area of deep action adaptation, we are presenting several benchmark results and techniques that could serve as baselines to guide future research in this area.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
EditorsHongdong Li, Greg Mori, Konrad Schindler, C.V. Jawahar
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783030208929
Publication statusE-pub ahead of print - 29 May 2019
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: 2 Dec 20186 Dec 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11363 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Asian Conference on Computer Vision, ACCV 2018


  • Action recognition
  • Domain adaptation
  • Transfer learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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