AdaBoost Based Multimodal Learning.

Abstract

This paper focuses on multi-modal learning and introduces an AdaBoost-based approach for multi-modal learning. We address two foundation problems, (1) the difference be-tween AdaBoost with homogeneous and heterogeneous weak learners; (2) generalization metric. By addressing these research questions, this paper enhances our under-standing of AdaBoost in the context of multi-modal learning through comprehensive experiments. The experiment results show that the heterogeneous structure is a trade-off between the performances of different weak learners rather than a clear synergy. The multi-modal learning model's performance depends on how the individual weak learners are composed, and the heterogeneous structure's ad-vantage lies in harnessing the diverse strengths of individual weak learners, even though the improvement achieved is not overwhelmingly pronounced.