A discriminative multi-modal adaptation neural network model for video action recognition.

Research on video-based understanding and learning has attracted widespread interest and has been adopted in various real applications, such as e-healthcare, action recognition, affective computing, to name a few. Amongst them, video-based action recognition is one of the most representative examples. With the advancement of multi-sensory technology, action recognition using multi-modal data has recently drawn wide attention. However, the research community faces new challenges in effectively exploring and utilizing the discriminative and complementary information across different modalities. Although score level fusion approaches have been popularly employed for multi-modal action recognition, they simply add the scores derived separately from different modalities without proper consideration of cross-modality semantics amongst multiple input data sources, invariably causing sub-optimal performance. To address this issue, this paper presents a two-stream heterogeneous network to extract and jointly process complementary features derived from RGB and skeleton modalities, respectively. Then, a discriminative multi-modal adaptation neural network model (DMANNM) is proposed and applied to the heterogeneous network, by integrating statistical machine learning (SML) principles with convolutional neural network (CNN) architecture. In addition, to achieve high recognition accuracy by the generated multi-modal structure, an effective nonlinear classification algorithm is presented in this work. Leveraging the joint strength of SML and CNN architecture, the proposed model forms an adaptive platform for handling datasets of different scales. To demonstrate the effectiveness and the generic nature of the proposed model, we conducted experiments on four popular video-based action recognition datasets with different scales: NTU RGB+D, NTU RGB+D 120, Northwestern-UCLA (N-UCLA), and SYSU. The experimental results show the superiority of the proposed method over state-of-the-art compared.