Tensor-empowered Incomplete Multimodal Learning with Modality Reconstruction for Edge Intelligence

The distributed computing paradigm of edge computing effectively addresses the challenges of data transmission delay and data privacy security. With the increasing popularity of IoT devices and 5G networks, edge computing has a broader range of applications. The advancement in artificial intelligence (AI) technology enables the realization of edge intelligence, which conducts data processing and analysis on edge devices to avoid excessive data transmission to the cloud, enhance system response speed, and protect user data privacy. In various edge intelligent systems like smart homes and autonomous driving, multimodal data plays a crucial role. However, missing modalities in such systems may lead to model failure in real-world environments. To tackle this issue, we propose a tensor-empowered modality reconstruction network (TMRN) that utilizes an end-to-end variational autoencoder for reconstructing missing modal data. This approach effectively enhances model robustness while reducing model size and training complexity. Furthermore, we introduce a supervised method for feature reconstruction to better align with the true distribution of missing modal data by leveraging tensor feature fusion and label supervision techniques. Additionally, we design a task information disentanglement module to make multimodal representations more relevant to specific tasks by effectively separating task-relevant from task-irrelevant information. Extensive experiments demonstrate that TMRN achieves competitive performance compared to existing state-of-the-art methods.