ON THE EFFECTIVENESS OF TASK GRANULARITY FOR TRANSFER LEARNING

Farzaneh Mahdisoltani, Guillaume Berger, Waseem Gharbieh, David Fleet, Roland Memisevic

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.

Keywords: Transfer Learning, Video Understanding, Fine-grained Video Classification, Video Captioning, Common Sense, Something-Something Dataset.

TL;DR: If the model architecture is fixed, how would the complexity and granularity of task, effect the quality of learned features for transferring to a new task.