2 citations · 2 across the 10 of their papers we have counts for
5 papers · 1 filter
Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing
Dominick Reilly, Qiyu Wu, Hiromi Wakaki +2
Multimodal Large Language Models (MLLMs) are typically designed under the assumption that all modalities available during training will also be accessible at inference. However, ma…
World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
Manish Kumar Govind, Dominick Reilly, Smit Patel +2
Going beyond predicting robot actions, World Action Models (WAMs) can also generate future visual observations. We build on this generative capability to propose Recurrent Generati…
TimeProVe: Propose, then Verify for Efficient Long Video Temporal Reasoning in Activities of Daily Living
Arkaprava Sinha, Dominick Reilly, Siddharth Krishnan +2
Long Video Question Answering (LVQA) requires identifying sparse, query-relevant evidence within hours-long untrimmed videos. Existing approaches either process videos densely with…
UNIEGO: Proxies as Mediators for Unified Egocentric Video Representation Learning
Wenhao Chi, Arkaprava Sinha, Dominick Reilly +2
Egocentric video understanding is inherently limited by the narrow perspective of wearable cameras: a single viewpoint, a single modality, a single model cannot capture the full ri…
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…