In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep …

omy of multimodal model architectures. Four distinct types of multimodal architec- ures and their sub-types are outlined. Various models are systematically.

In robotics, multimodal models allow a machine to observe, reason, and act in real-world, dynamic environments. Agents like PaLM-E [7] use language commands, RGB-D vision, proprioceptive …

Blended learning increasingly is seen as one of the important pedagogical approaches that can help in this regard. The purpose of this article is to propose a blending with purpose …

To address these challenges, we developed SleepFM, a multimodal sleep foundation model trained with a new contrastive learning approach that accommodates multiple PSG …

model is a model developed by OpenAI for generating video. It utilizes multimodal learning techniques to generat realistic video content by combining text and image data. In recent …

Taking a probabilistic perspective, we explore two model-ing paradigms for multimodal learning: multidimensional modeling, which treats the features as a single modality and multimodal …