4 citations · 6 across the 14 of their papers we have counts for
14 papers
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…
VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models
Dominick Reilly, Manish Kumar Govind, Le Xue +1
Large Vision Language Models (VLMs) excel at general visual reasoning but experience significant performance degradation when deployed in novel domains that exhibit substantial dis…
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…
From My View to Yours: Learning Egocentric Cues from Exocentric Video using Privileged Egocentric Supervision
Dominick Reilly, Manish Kumar Govind, Le Xue +1
Vision Language Models (VLMs) have achieved strong performance across a wide range of video understanding tasks. However, their viewpoint-invariant training limits their ability to…
Introducing Gating and Context into Temporal Action Detection
Aglind Reka, Diana Laura Borza, Dominick Reilly +2
Temporal Action Detection (TAD), the task of localizing and classifying actions in untrimmed video, remains challenging due to action overlaps and variable action durations. Recent…
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across Heads
Ali K. Rahimian, Manish K. Govind, Subhajit Maity +4
Vision Transformers and their variants have achieved remarkable success in diverse visual perception tasks. Despite their effectiveness, they suffer from two significant limitation…
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…
Just Add ! Pose Induced Video Transformers for Understanding Activities of Daily Living
Dominick Reilly, Srijan Das
Video transformers have become the de facto standard for human action recognition, yet their exclusive reliance on the RGB modality still limits their adoption in certain domains.…
Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders
Srijan Das, Tanmay Jain, Dominick Reilly +6
Vision Transformers (ViTs) have become ubiquitous in computer vision. Despite their success, ViTs lack inductive biases, which can make it difficult to train them with limited data…
Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers
Dominick Reilly, Aman Chadha, Srijan Das
Human perception of surroundings is often guided by the various poses present within the environment. Many computer vision tasks, such as human action recognition and robot imitati…