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Article overview
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Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition | | Tailin Chen
; Desen Zhou
; Jian Wang
; Shidong Wang
; Qian He
; Chuanyang Hu
; Errui Ding
; Yu Guan
; Xuming He
; | | Date: |
19 Aug 2022 | | Abstract: | In this paper, we study the problem of one-shot skeleton-based action
recognition, which poses unique challenges in learning transferable
representation from base classes to novel classes, particularly for
fine-grained actions. Existing meta-learning frameworks typically rely on the
body-level representations in spatial dimension, which limits the
generalisation to capture subtle visual differences in the fine-grained label
space. To overcome the above limitation, we propose a part-aware prototypical
representation for one-shot skeleton-based action recognition. Our method
captures skeleton motion patterns at two distinctive spatial levels, one for
global contexts among all body joints, referred to as body level, and the other
attends to local spatial regions of body parts, referred to as the part level.
We also devise a class-agnostic attention mechanism to highlight important
parts for each action class. Specifically, we develop a part-aware prototypical
graph network consisting of three modules: a cascaded embedding module for our
dual-level modelling, an attention-based part fusion module to fuse parts and
generate part-aware prototypes, and a matching module to perform classification
with the part-aware representations. We demonstrate the effectiveness of our
method on two public skeleton-based action recognition datasets: NTU RGB+D 120
and NW-UCLA. | | Source: | arXiv, 2208.09150 | | Services: | Forum | Review | PDF | Favorites | |
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