5 accepted papers
Can interactive vision-and-language agents learn not just what to say but also when to say it? Current language models rarely plan over whether and when to realize a real-time response to a user. However, providing accurate and timely support for human decision-making, such as when guiding visually …
Models for student reading performance can empower educators and institutions to proactively identify at-risk students, thereby enabling early and tailored instructional interventions. However, there are no suitable publicly available educational datasets for modeling and predicting future reading p…
"Embodied vision-based real-world systems, such as mobile robots, require a careful balance between energy consumption, compute latency, and safety constraints to optimize operation across dynamic tasks and contexts. As local computation tends to be restricted, offloading the computation, , to a rem…
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 settings. In contrast to standard monocular VO approaches which often study a known calibration within a single dataset, XVO…
"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. Towards exploring real-time information needs and fundamental challenges in our novel modeling task, we first collect a mult…