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Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning
[article]
2021
pre-print
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
doi:10.1145/3474085.3475216
arXiv:2108.06647v1
fatcat:vqgnlnojwzabpk7fqcmwfadtuu