Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Multimodal Vision Language Models (VLMs) have emerged as a transformative technology
at the intersection of computer vision and natural language processing, enabling machines …

We propose XVO, a semi-supervised learning method for training generalized monocular
Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and …

Effectively utilizing the vast amounts of ego-centric navigation data that is freely available on
the internet can advance generalized intelligent systems, ie, to robustly scale across …

We introduce a novel vision-and-language navigation (VLN) task of learning to provide real-
time guidance to a blind follower situated in complex dynamic navigation scenarios …

Abstract We introduce Motion Diversification Networks a novel framework for learning to
generate realistic and diverse 3D human motion. Despite recent advances in deep …

While behavior cloning has recently emerged as a highly successful paradigm for
autonomous driving humans rarely learn to perform complex tasks such as driving via …

Embodied vision-based real-world systems, such as mobile robots, require a careful
balance between energy consumption, compute latency, and safety constraints to optimize …

People who are blind perceive the world differently than those who are sighted, which can
result in distinct motion characteristics. For instance, when crossing at an intersection, blind …

Models for student reading performance can empower educators and institutions to
proactively identify at-risk students, thereby enabling early and tailored instructional …