  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 a
neural net 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.
