Few-shot action recognition aims to recognize novel action classes (query)
using just a few samples (support). The majority of current approaches follow
the metric learning paradigm, which learns to compare the similarity between
videos. Recently, it has been observed that directly measuring this similarity
is not ideal since different action instances may show distinctive temporal
distribution, resulting in severe misalignment issues across query and support
videos. In this paper, we arrest this problem from two distinct aspects --
action duration misalignment and action evolution misalignment. We address them
sequentially through a Two-stage Action Alignment Network (TA2N). The first
stage locates the action by learning a temporal affine transform, which warps
each video feature to its action duration while dismissing the
action-irrelevant feature (e.g. background). Next, the second stage coordinates
query feature to match the spatial-temporal action evolution of support by
performing temporally rearrange and spatially offset prediction. Extensive
experiments on benchmark datasets show the potential of the proposed method in
achieving state-of-the-art performance for few-shot action recognition.The code
of this project can be found at this https URL

本文提出了一个基于 Two-stage Action Alignment Network (TA2N) 的方法，通过学习时间仿射变换来定位动作并解决时空分布不一致的问题，从而在几个样本的情况下识别新的动作类别，并在基准数据集上实现了最先进的表现。