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.
@article{arxiv.2204.07270,
title = {Model-agnostic Multi-Domain Learning with Domain-Specific Adapters for Action Recognition},
author = {Kazuki Omi and Jun Kimata and Toru Tamaki},
journal= {arXiv preprint arXiv:2204.07270},
year = {2022}
}
Comments
IEICE Transactions on Information and Systems, Vol. E105-D, No. 12, Dec. 2022