  In this paper we propose a novel Temporal Attentive Relation Network (TARN)
for the problems of few-shot and zero-shot action recognition. At the heart of
our network is a meta-learning approach that learns to compare representations
of variable temporal length, that is, either two videos of different length (in
the case of few-shot action recognition) or a video and a semantic
representation such as word vector (in the case of zero-shot action
recognition). By contrast to other works in few-shot and zero-shot action
recognition, we a) utilise attention mechanisms so as to perform temporal
alignment, and b) learn a deep-distance measure on the aligned representations
at video segment level. We adopt an episode-based training scheme and train our
network in an end-to-end manner. The proposed method does not require any
fine-tuning in the target domain or maintaining additional representations as
is the case of memory networks. Experimental results show that the proposed
architecture outperforms the state of the art in few-shot action recognition,
and achieves competitive results in zero-shot action recognition.
