title: ON THE EFFECTIVENESS OF TASK GRANULARITY FOR TRANSFER LEARNING
abstract: We describe a DNN for video classification and captioning, trained end-to-end,
  with shared features, to solve tasks at different levels of granularity, exploring the
  link between granularity in a source task and the quality of learned features for
  transfer learning. For solving the new task domain in transfer learning, we freeze
  the trained encoder and fine-tune an MLP on the target domain. We train on the
  Something-Something dataset with over 220, 000 videos, and multiple levels of
  target granularity, including 50 action groups, 174 fine-grained action categories
  and captions. Classification and captioning with Something-Something are challenging
  because of the subtle differences between actions, applied to thousands
  of different object classes, and the diversity of captions penned by crowd actors.
  Our model performs better than existing classification baselines for SomethingSomething,
  with impressive fine-grained results. And it yields a strong baseline on
  the new Something-Something captioning task. Experiments reveal that training
  with more fine-grained tasks tends to produce better features for transfer learning.
authors: Farzaneh Mahdisoltani Guillaume Berger Waseem Gharbieh David Fleet Roland Memisevic
hIndex: Roland Memisevic 35