Class-Incremental Gesture Recognition Learning with Out-of-Distribution Detection

Gesture recognition is a popular human-computer interaction technology, which has been widely applied in many fields (e.g., autonomous driving, medical care, VR and AR). However, 1) most existing gesture recognition methods focus on the fixed recognition scenarios with several gestures, which could lead to memory consumption and computational effort when continuously learning new gestures; 2) Meanwhile, the performance of popular class-incremental methods degrades significantly for previously learned classes (i.e., catastrophic forgetting) due to the ambiguity and variability of gestures. To tackle these challenges, we propose a novel class-incremental gesture recognition method with out-of-distribution (OOD) detection, which can continuously adapt to new gesture classes and achieve high performance for both learned and new gestures. Specifically, we construct an episodic memory with a subset of learned training samples to preserve the previous knowledge from forgetting. Moreover, the OOD detection-based memory management is developed for exploring the most representative and informative core set from the learned datasets. When a new gesture recognition task with strange classes comes, rehearsal enhancement is adopted to increase the diversity of memory exemplars for better fitting the real characteristics of gesture recognition. After deriving an effective class-incremental gesture recognition strategy, we perform experiments on two representative datasets to validate the superiority of our method. Evaluation experiments demonstrate that our proposed method substantially outperforms the state-of-the-art methods with about 2.17%-3.81% improvement under different class-incremental learning scenarios.