Human action video recognition has recently attracted more attention in
applications such as video security and sports posture correction. Popular
solutions, including graph convolutional networks (GCNs) that model the human
skeleton as a spatiotemporal graph, have proven very effective. GCNs-based
methods with stacked blocks usually utilize top-layer semantics for
classification/annotation purposes. Although the global features learned
through the procedure are suitable for the general classification, they have
difficulty capturing fine-grained action change across adjacent frames --
decisive factors in sports actions. In this paper, we propose a novel
``Cross-block Fine-grained Semantic Cascade (CFSC)'' module to overcome this
challenge. In summary, the proposed CFSC progressively integrates shallow
visual knowledge into high-level blocks to allow networks to focus on action
details. In particular, the CFSC module utilizes the GCN feature maps produced
at different levels, as well as aggregated features from proceeding levels to
consolidate fine-grained features. In addition, a dedicated temporal
convolution is applied at each level to learn short-term temporal features,
which will be carried over from shallow to deep layers to maximize the leverage
of low-level details. This cross-block feature aggregation methodology, capable
of mitigating the loss of fine-grained information, has resulted in improved
performance. Last, FD-7, a new action recognition dataset for fencing sports,
was collected and will be made publicly available. Experimental results and
empirical analysis on public benchmarks (FSD-10) and self-collected (FD-7)
demonstrate the advantage of our CFSC module on learning discriminative
patterns for action classification over others.

本文提出了一种名为 “交叉块细粒度语义级联（CFSC）” 的模块来改善人体动作视频识别的问题，并在实验结果中展示了 CFSC 模块在动作分类方面的优势。