Decoupled Prompt-Adapter Tuning for Continual Activity Recognition

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. The widespread deployment of cameras has significantly broadened the scope and influence of action recognition technology across multiple sectors. This technology is essential for boosting safety via security and surveillance, offering vital patient care in healthcare, and providing in-depth performance analyses in sports (Kong & Fu, 2022). It also enables robots to quickly perceive and respond to human actions during human-AI collaborations (Akkaladevi & Heindl, 2015), thereby enhancing the collaboration and efficiency between humans and AI in contexts such as manufacturing and assistive technologies.