  Few-shot action recognition has attracted increasing attention due to the
difficulty in acquiring the properly labelled training samples. Current works
have shown that preserving spatial information and comparing video descriptors
are crucial for few-shot action recognition. However, the importance of
preserving temporal information is not well discussed. In this paper, we
propose a Contents and Length-based Temporal Attention (CLTA) model, which
learns customized temporal attention for the individual video to tackle the
few-shot action recognition problem. CLTA utilizes the Gaussian likelihood
function as the template to generate temporal attention and trains the learning
matrices to study the mean and standard deviation based on both frame contents
and length. We show that even a not fine-tuned backbone with an ordinary
softmax classifier can still achieve similar or better results compared to the
state-of-the-art few-shot action recognition with precisely captured temporal
attention.
