Action Class Relation Detection and Classification Across Multiple Video
  Datasets
Yuya Yoshikawa Yutaro Shigeto Masashi Shimbo Akikazu Takeuchi
The Meta Video Dataset (MetaVD) provides annotated relations between action
classes in major datasets for human action recognition in videos. Although
these annotated relations enable dataset augmentation, it is only applicable to
those covered by MetaVD. For an external dataset to enjoy the same benefit, the
relations between its action classes and those in MetaVD need to be determined.
To address this issue, we consider two new machine learning tasks: action class
relation detection and classification. We propose a unified model to predict
relations between action classes, using language and visual information
associated with classes. Experimental results show that (i) pre-trained recent
neural network models for texts and videos contribute to high predictive
performance, (ii) the relation prediction based on action label texts is more
accurate than based on videos, and (iii) a blending approach that combines
predictions by both modalities can further improve the predictive performance
in some cases.