  Action recognition technology plays a vital role in enhancing security
through surveillance systems, enabling better patient monitoring in healthcare,
providing in-depth performance analysis in sports, and facilitating seamless
human-AI collaboration in domains such as manufacturing and assistive
technologies. The dynamic nature of data in these areas underscores the need
for models that can continuously adapt to new video data without losing
previously acquired knowledge, highlighting the critical role of advanced
continual action recognition. To address these challenges, we propose Decoupled
Prompt-Adapter Tuning (DPAT), a novel framework that integrates adapters for
capturing spatial-temporal information and learnable prompts for mitigating
catastrophic forgetting through a decoupled training strategy. DPAT uniquely
balances the generalization benefits of prompt tuning with the plasticity
provided by adapters in pretrained vision models, effectively addressing the
challenge of maintaining model performance amidst continuous data evolution
without necessitating extensive finetuning. DPAT consistently achieves
state-of-the-art performance across several challenging action recognition
benchmarks, thus demonstrating the effectiveness of our model in the domain of
continual action recognition.
