  Analysis and interpretation of egocentric video data is becoming more and
more important with the increasing availability and use of wearable cameras.
Exploring and fully understanding affinities and differences between ego and
allo (or third-person) vision is paramount for the design of effective methods
to process, analyse and interpret egocentric data. In addition, a deeper
understanding of ego-vision and its peculiarities may enable new research
perspectives in which first person viewpoints can act either as a mean for
easily acquiring large amounts of data to be employed in general-purpose
recognition systems, and as a challenging test-bed to assess the usability of
techniques specifically tailored to deal with allocentric vision on more
challenging settings. Our work, with an eye to cognitive science findings,
leverages transfer learning in Convolutional Neural Networks to demonstrate
capabilities and limitations of an implicitly learnt view-invariant
representation in the specific case of action recognition.
