1 citations · 2 across the 5 of their papers we have counts for
5 papers
UniLACT: Depth-Aware RGB Latent Action Learning for Vision-Language-Action Models
Manish Kumar Govind, Dominick Reilly, Pu Wang +1
Latent action representations learned from unlabeled videos have recently emerged as a promising paradigm for pretraining vision-language-action (VLA) models without explicit robot…
SKI Models: Skeleton Induced Vision-Language Embeddings for Understanding Activities of Daily Living
Arkaprava Sinha, Dominick Reilly, Francois Bremond +2
The introduction of vision-language models like CLIP has enabled the development of foundational video models capable of generalizing to unseen videos and human actions. However, t…
MS-Temba: Multi-Scale Temporal Mamba for Understanding Long Untrimmed Videos
Arkaprava Sinha, Monish Soundar Raj, Pu Wang +3
Temporal Action Detection (TAD) in untrimmed videos poses significant challenges, particularly for Activities of Daily Living (ADL) requiring models to (1) process long-duration vi…
LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living
Dominick Reilly, Rajatsubhra Chakraborty, Arkaprava Sinha +5
Current Large Language Vision Models (LLVMs) trained on web videos perform well in general video understanding but struggle with fine-grained details, complex human-object interact…
MUG: Interactive Multimodal Grounding on User Interfaces
Tao Li, Gang Li, Jingjie Zheng +2
We present MUG, a novel interactive task for multimodal grounding where a user and an agent work collaboratively on an interface screen. Prior works modeled multimodal UI grounding…