13 citations · 13 across the 7 of their papers we have counts for
8 papers
JUICE: Unified Multimodal Modeling in Pretrained Representation Spaces
Jiahan Li, Huangyuan Su, Shengbang Tong +9
Most unified multimodal models treat images and text asymmetrically: a pretrained language model generates text, an attached diffusion head generates images, and outputs that conta…
PaintBench: Deterministic Evaluation of Precise Visual Editing
Kai Xu, Ellis Brown, Shrikar Madhu +3
While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle. To probe this challenge, we introd…
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…
Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders
Shengbang Tong, Boyang Zheng, Ziteng Wang +7
Representation Autoencoders (RAEs) have shown distinct advantages in diffusion modeling on ImageNet by training in high-dimensional semantic latent spaces. In this work, we investi…
SIMS-V: Simulated Instruction-Tuning for Spatial Video Understanding
Ellis Brown, Arijit Ray, Ranjay Krishna +3
Despite impressive high-level video comprehension, multimodal language models struggle with spatial reasoning across time and space. While current spatial training approaches rely…
Cambrian-S: Towards Spatial Supersensing in Video
Shusheng Yang, Jihan Yang, Pinzhi Huang +12
We argue that progress in true multimodal intelligence calls for a shift from reactive, task-driven systems and brute-force long context towards a broader paradigm of supersensing.…
Benchmark Designers Should "Train on the Test Set" to Expose Exploitable Non-Visual Shortcuts
Ellis Brown, Jihan Yang, Shusheng Yang +2
Multimodal LLMs can answer many questions in vision-centric benchmarks without looking at the image, using linguistic priors and skewed answer distributions. Blind evaluation shows…
Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs
Shengbang Tong, Ellis Brown, Penghao Wu +11
We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. While stronger language models can enhance multimodal capabilities, the design…