In this paper, we propose a multi-domain learning model for action recognition. The proposed method inserts domain-specific adapters between layers of domain-independent layers of a backbone network. Unlike a multi-head network that switches classification heads only, our model switches not only the heads, but also the adapters for facilitating to learn feature representations universal to multiple domains. Unlike prior works, the proposed method is model-agnostic and doesn't assume model structures unlike prior works. Experimental results on three popular action recognition datasets (HMDB51, UCF101, and Kinetics-400) demonstrate that the proposed method is more effective than a multi-head architecture and more efficient than separately training models for each domain.

This paper presents a novel current driving method for the synchronous rectifier (SR) in a Flyback topology. Compared to the previous proposed Current Driven Synchronous Rectifier (CDSR), the proposed CDSR features simple structure, low power loss and good performance. The proposed SR driving method is implemented in a 64 W Flyback converter with universal input, and efficiency as high as 92.5% is achieved at low input (90 V ac) and full load condition.