Unified Contrastive Fusion Transformer for Multimodal Human Action
  Recognition
Kyoung Ok Yang Junho Koh Jun Won Choi
Various types of sensors have been considered to develop human action
recognition (HAR) models. Robust HAR performance can be achieved by fusing
multimodal data acquired by different sensors. In this paper, we introduce a
new multimodal fusion architecture, referred to as Unified Contrastive Fusion
Transformer (UCFFormer) designed to integrate data with diverse distributions
to enhance HAR performance. Based on the embedding features extracted from each
modality, UCFFormer employs the Unified Transformer to capture the
inter-dependency among embeddings in both time and modality domains. We present
the Factorized Time-Modality Attention to perform self-attention efficiently
for the Unified Transformer. UCFFormer also incorporates contrastive learning
to reduce the discrepancy in feature distributions across various modalities,
thus generating semantically aligned features for information fusion.
Performance evaluation conducted on two popular datasets, UTD-MHAD and NTU
RGB+D, demonstrates that UCFFormer achieves state-of-the-art performance,
outperforming competing methods by considerable margins.