5 citations · 5 across the 6 of their papers we have counts for
8 papers
Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes
Junlin Han, Shengbang Tong, David Fan +4
Vision offers a critical axis for advancing foundation models, driving a shift towards natively unified multimodal pretraining. Despite this momentum, the design space and the fund…
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…
A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures
Basile Terver, Randall Balestriero, Megi Dervishi +8
We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEPAs). JEPAs learn to predict in represent…
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…
Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training
Junlin Han, Shengbang Tong, David Fan +4
Large Language Models (LLMs), despite being trained on text alone, surprisingly develop rich visual priors. These priors allow latent visual capabilities to be unlocked for vision…
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan +27
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-s…
Scaling Language-Free Visual Representation Learning
David Fan, Shengbang Tong, Jiachen Zhu +8
Visual Self-Supervised Learning (SSL) currently underperforms Contrastive Language-Image Pretraining (CLIP) in multimodal settings such as Visual Question Answering (VQA). This mul…
MetaMorph: Multimodal Understanding and Generation via Instruction Tuning
Shengbang Tong, David Fan, Jiachen Zhu +7
In this work, we propose Visual-Predictive Instruction Tuning (VPiT) - a simple and effective extension to visual instruction tuning that enables a pretrained LLM to quickly morph…