Zhang Tingting, Fu Qunhang, Ding Han, Wang Ge, Wang Fei
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2025 Jul 30;25(15):4706. doi: 10.3390/s25154706.
In real-world applications, user demands for new functionalities and activities constantly evolve, requiring action recognition systems to incrementally incorporate new action classes without retraining from scratch. This class-incremental learning (CIL) paradigm is essential for enabling adaptive and scalable systems that can grow over time. However, Wi-Fi-based indoor action recognition under incremental learning faces two major challenges: catastrophic forgetting of previously learned knowledge and uncontrolled model expansion as new classes are added. To address these issues, we propose CAREC, a class-incremental framework that balances dynamic model expansion with efficient compression. CAREC adopts a multi-branch architecture to incorporate new classes without compromising previously learned features and leverages balanced knowledge distillation to compress the model by 80% while preserving performance. A data replay strategy retains representative samples of old classes, and a super-feature extractor enhances inter-class discrimination. Evaluated on the large-scale XRF55 dataset, CAREC reduces performance degradation by 51.82% over four incremental stages and achieves 67.84% accuracy with only 21.08 M parameters, 20% parameters compared to conventional approaches.
在实际应用中，用户对新功能和活动的需求不断演变，这就要求动作识别系统在不从头重新训练的情况下逐步纳入新的动作类别。这种类别增量学习（CIL）范式对于实现能够随时间增长的自适应和可扩展系统至关重要。然而，增量学习下基于Wi-Fi的室内动作识别面临两个主要挑战：对先前学习知识的灾难性遗忘以及随着新类别添加而不受控制的模型扩展。为了解决这些问题，我们提出了CAREC，这是一个类别增量框架，它在动态模型扩展与高效压缩之间取得平衡。CAREC采用多分支架构来纳入新类别，同时不损害先前学习的特征，并利用平衡知识蒸馏将模型压缩80%，同时保持性能。数据重放策略保留旧类别的代表性样本，超特征提取器增强类间区分能力。在大规模XRF55数据集上进行评估时，CAREC在四个增量阶段将性能下降降低了51.82%，并且仅用2108万个参数就达到了67.84%的准确率，与传统方法相比参数减少了20%。
