S-Omninet: Structured Data Enhanced Universal Multimodal Learning Architecture
This work addresses the challenge of integrating structured data with unstructured modalities for researchers in multimodal learning, though it is incremental as it builds on an existing architecture.
The authors tackled the problem of multimodal multitask learning by extending Omninet to handle structured data, achieving significant improvements over the baseline Omninet on several multimodal datasets.
Multimodal multitask learning has attracted an increasing interest in recent years. Singlemodal models have been advancing rapidly and have achieved astonishing results on various tasks across multiple domains. Multimodal learning offers opportunities for further improvements by integrating data from multiple modalities. Many methods are proposed to learn on a specific type of multimodal data, such as vision and language data. A few of them are designed to handle several modalities and tasks at a time. In this work, we extend and improve Omninet, an architecture that is capable of handling multiple modalities and tasks at a time, by introducing cross-cache attention, integrating patch embeddings for vision inputs, and supporting structured data. The proposed Structured-data-enhanced Omninet (S-Omninet) is a universal model that is capable of learning from structured data of various dimensions effectively with unstructured data through cross-cache attention, which enables interactions among spatial, temporal, and structured features. We also enhance spatial representations in a spatial cache with patch embeddings. We evaluate the proposed model on several multimodal datasets and demonstrate a significant improvement over the baseline, Omninet.