11 citations · 17 across the 8 of their papers we have counts for
8 papers
CubePart: An Open-Vocabulary Part-Controllable 3D Generator
Yiheng Zhu, Kangle Deng, Jean-Philippe Fauconnier +9
Interactive 3D assets used in games and simulation are typically decomposed into specific semantic parts to support animation, physics, and scripted behaviors, yet most generative…
TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos
Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang +7
Videos are unique in their integration of temporal elements, including camera, scene, action, and attribute, along with their dynamic relationships over time. However, existing ben…
Cube: A Roblox View of 3D Intelligence
Foundation AI Team, Kiran Bhat, Nishchaie Khanna +44
Foundation models trained on vast amounts of data have demonstrated remarkable reasoning and generation capabilities in the domains of text, images, audio and video. Our goal at Ro…
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
Haotian Zhang, Mingfei Gao, Zhe Gan +20
We present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and mu…
Understanding Alignment in Multimodal LLMs: A Comprehensive Study
Elmira Amirloo, Jean-Philippe Fauconnier, Christoph Roesmann +8
Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains…
MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs
Yusu Qian, Hanrong Ye, Jean-Philippe Fauconnier +3
We introduce MIA-Bench, a new benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to strictly adhere to complex instructions. Our benchmark com…
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier +29
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. T…
Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer
Benjamin Muller, Deepanshu Gupta, Siddharth Patwardhan +3
Multi-lingual language models (LM), such as mBERT, XLM-R, mT5, mBART, have been remarkably successful in enabling natural language tasks in low-resource languages through cross-lin…