EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge: Mixed
  Sequences Prediction
Amirshayan Nasirimajd Simone Alberto Peirone Chiara Plizzari Barbara Caputo
This report presents the technical details of our approach for the
EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action
Recognition. Our approach is based on the idea that the order in which actions
are performed is similar between the source and target domains. Based on this,
we generate a modified sequence by randomly combining actions from the source
and target domains. As only unlabelled target data are available under the UDA
setting, we use a standard pseudo-labeling strategy for extracting action
labels for the target. We then ask the network to predict the resulting action
sequence. This allows to integrate information from both domains during
training and to achieve better transfer results on target. Additionally, to
better incorporate sequence information, we use a language model to filter
unlikely sequences. Lastly, we employed a co-occurrence matrix to eliminate
unseen combinations of verbs and nouns. Our submission, labeled as 'sshayan',
can be found on the leaderboard, where it currently holds the 2nd position for
'verb' and the 4th position for both 'noun' and 'action'.