  Fine-grained action recognition is attracting increasing attention due to the
emerging demand of specific action understanding in real-world applications,
whereas the data of rare fine-grained categories is very limited. Therefore, we
propose the few-shot fine-grained action recognition problem, aiming to
recognize novel fine-grained actions with only few samples given for each
class. Although progress has been made in coarse-grained actions, existing
few-shot recognition methods encounter two issues handling fine-grained
actions: the inability to capture subtle action details and the inadequacy in
learning from data with low inter-class variance. To tackle the first issue, a
human vision inspired bidirectional attention module (BAM) is proposed.
Combining top-down task-driven signals with bottom-up salient stimuli, BAM
captures subtle action details by accurately highlighting informative
spatio-temporal regions. To address the second issue, we introduce contrastive
meta-learning (CML). Compared with the widely adopted ProtoNet-based method,
CML generates more discriminative video representations for low inter-class
variance data, since it makes full use of potential contrastive pairs in each
training episode. Furthermore, to fairly compare different models, we establish
specific benchmark protocols on two large-scale fine-grained action recognition
datasets. Extensive experiments show that our method consistently achieves
state-of-the-art performance across evaluated tasks.
