11 papers
V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
Lorenzo Mur-Labadia, Matthew Muckley, Amir Bar +6
We present V-JEPA 2.1, a family of self-supervised models that learn dense, high-quality visual representations for both images and videos while retaining strong global scene under…
PGT: Procedurally Generated Tasks for improving visual grounding in MLLMs
Rim Assouel, Amir Bar, Michal Drozdzal +1
Despite remarkable progress in Multimodal Large Language Models (MLLMs), these models still struggle with fine-grained understanding tasks. In this work, we propose Procedurally Ge…
World Models for Learning Dexterous Hand-Object Interactions from Human Videos
Raktim Gautam Goswami, Amir Bar, David Fan +6
Modeling dexterous hand-object interactions is challenging as it requires understanding how subtle finger motions influence the environment through contact with objects. While rece…
Grounding Generated Videos in Feasible Plans via World Models
Christos Ziakas, Amir Bar, Alessandra Russo
Large-scale video generative models have shown emerging capabilities as zero-shot visual planners, yet video-generated plans often violate temporal consistency and physical constra…
Beyond Language Modeling: An Exploration of Multimodal Pretraining
Shengbang Tong, David Fan, John Nguyen +18
The visual world offers a critical axis for advancing foundation models beyond language. Despite growing interest in this direction, the design space for native multimodal models r…
Parallel Stochastic Gradient-Based Planning for World Models
Michael Psenka, Michael Rabbat, Aditi Krishnapriyan +2
World models simulate environment dynamics from raw sensory inputs like video. However, using them for planning can be challenging due to the vast and unstructured search space. We…