7 citations · 8 across the 4 of their papers we have counts for
4 papers
User-in-the-loop Evaluation of Multimodal LLMs for Activity Assistance
Mrinal Verghese, Brian Chen, Hamid Eghbalzadeh +2
Our research investigates the capability of modern multimodal reasoning models, powered by Large Language Models (LLMs), to facilitate vision-powered assistants for multi-step dail…
Unlocking Exocentric Video-Language Data for Egocentric Video Representation Learning
Zi-Yi Dou, Xitong Yang, Tushar Nagarajan +5
We present EMBED (Egocentric Models Built with Exocentric Data), a method designed to transform exocentric video-language data for egocentric video representation learning. Large-s…
Detours for Navigating Instructional Videos
Kumar Ashutosh, Zihui Xue, Tushar Nagarajan +1
We introduce the video detours problem for navigating instructional videos. Given a source video and a natural language query asking to alter the how-to video's current path of exe…
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
Seungwhan Moon, Andrea Madotto, Zhaojiang Lin +10
We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and…