  Spatial and temporal modeling is one of the most core aspects of few-shot
action recognition. Most previous works mainly focus on long-term temporal
relation modeling based on high-level spatial representations, without
considering the crucial low-level spatial features and short-term temporal
relations. Actually, the former feature could bring rich local semantic
information, and the latter feature could represent motion characteristics of
adjacent frames, respectively. In this paper, we propose SloshNet, a new
framework that revisits the spatial and temporal modeling for few-shot action
recognition in a finer manner. First, to exploit the low-level spatial
features, we design a feature fusion architecture search module to
automatically search for the best combination of the low-level and high-level
spatial features. Next, inspired by the recent transformer, we introduce a
long-term temporal modeling module to model the global temporal relations based
on the extracted spatial appearance features. Meanwhile, we design another
short-term temporal modeling module to encode the motion characteristics
between adjacent frame representations. After that, the final predictions can
be obtained by feeding the embedded rich spatial-temporal features to a common
frame-level class prototype matcher. We extensively validate the proposed
SloshNet on four few-shot action recognition datasets, including
Something-Something V2, Kinetics, UCF101, and HMDB51. It achieves favorable
results against state-of-the-art methods in all datasets.
