# Video Domain Incremental Learning for Human Action Recognition in Home Environments Source: [Video Domain Incremental Learning for Human Action Recognition in Home Environments]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:44:57.967Z Authors: Yuanda Hu, Xing Liu, Meiying Li, Yate Ge, Xiaohua Sun, Weiwei Guo Published: 2024-12-22T09:40:48.000Z ## Abstract It is significantly challenging to recognize daily human actions in homes due to the diversity and dynamic changes in unconstrained home environments. It spurs the need to continually adapt to various users and scenes. Fine-tuning current video understanding models on newly encountered domains often leads to catastrophic forgetting, where the models lose their ability to perform well on previously learned scenarios. To address this issue, we formalize the problem of Video Domain Incremental Learning (VDIL), which enables models to learn continually from different domains while maintaining a fixed set of action classes. Existing continual learning research primarily focuses on class-incremental learning, while the domain incremental learning has been largely overlooked in video understanding. In this work, we introduce a novel benchmark of domain incremental human action recognition for unconstrained home environments. We design three domain split types (user, scene, hybrid) to systematically assess the challenges posed by domain shifts in real-world home settings. Furthermore, we propose a baseline learning strategy based on replay and reservoir sampling techniques without domain labels to handle scenarios with limited memory and task agnosticism. Extensive experimental results demonstrate that our simple sampling and replay strategy outperforms most existing continual learning methods across the three proposed benchmarks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Action Recognition on NTU-RGB+D 120](): Action Recognition; NTU RGB+D 120; FAA: 67.3. - [Semantic Segmentation on Domains Campus](): Semantic segmentation; Domains Campus; FAA: 43. ## Original sources and implementation [Original paper]() [PDF]()