  Humans can easily recognize actions with only a few examples given, while the
existing video recognition models still heavily rely on the large-scale labeled
data inputs. This observation has motivated an increasing interest in few-shot
video action recognition, which aims at learning new actions with only very few
labeled samples. In this paper, we propose a depth guided Adaptive Meta-Fusion
Network for few-shot video recognition which is termed as AMeFu-Net.
Concretely, we tackle the few-shot recognition problem from three aspects:
firstly, we alleviate this extremely data-scarce problem by introducing depth
information as a carrier of the scene, which will bring extra visual
information to our model; secondly, we fuse the representation of original RGB
clips with multiple non-strictly corresponding depth clips sampled by our
temporal asynchronization augmentation mechanism, which synthesizes new
instances at feature-level; thirdly, a novel Depth Guided Adaptive Instance
Normalization (DGAdaIN) fusion module is proposed to fuse the two-stream
modalities efficiently. Additionally, to better mimic the few-shot recognition
process, our model is trained in the meta-learning way. Extensive experiments
on several action recognition benchmarks demonstrate the effectiveness of our
model.
