Majority of research in learning based methods has been towards designing and training
networks for specific tasks. However, many of the learning based tasks, across modalities …

Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data
(eg, images, texts, or data collected from different sensors), feature engineering (eg …

This paper introduces a novel task called Cross Modal Generalization (CMG), which
addresses the challenge of learning a unified discrete representation from paired …

Abstract Vision-Language models (VLMs) have excelled in the image-domain---especially in
zero-shot settings---thanks to the availability of vast pretraining data (ie paired image-text …

In the context of computer vision and human-robot interaction forecasting 3D human poses
is crucial for understanding human behavior and enhancing the predictive capabilities of …

Multimodal learning assumes all modality combinations of interest are available during
training to learn cross-modal correspondences. In this paper, we challenge this modality …

We present a novel multimodal multitask network and associated training algorithm. The
method is capable of ingesting data from approximately 12 different modalities namely …

Augmented Reality (AR) devices, emerging as prominent mobile interaction platforms, face
challenges in user safety, particularly concerning oncoming vehicles. While some solutions …

Brain tumor segmentation is a fundamental task and existing approaches usually rely on
multi-modality magnetic resonance imaging (MRI) images for accurate segmentation …

Training deep learning models for video classification from audio-visual data commonly
requires vast amounts of labeled training data collected via a costly process. A challenging …