43 citations · 64 across the 22 of their papers we have counts for
10 papers · 1 filter
MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Yanghao Li, Rui Qian, Bowen Pan +24
Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from…
Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning
Haoji Zhang, Xin Gu, Jiawen Li +7
The video reasoning ability of multimodal large language models (MLLMs) is crucial for downstream tasks like video question answering and temporal grounding. While recent approache…
DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture Design in Text to Image Generation
Chen Chen, Rui Qian, Wenze Hu +8
In this work, we empirically study Diffusion Transformers (DiTs) for text-to-image generation, focusing on architectural choices, text-conditioning strategies, and training protoco…
CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling
Xinze Wang, Chen Chen, Yinfei Yang +5
Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. While integrating MoE into multimodal models like CLIP improves performanc…
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation
Xinyang Huang, Chuang Zhu, Bowen Zhang +1
Semi-supervised domain adaptation (SSDA) has been widely studied due to its ability to utilize a few labeled target data to improve the generalization ability of the model. However…
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
Zhengfeng Lai, Vasileios Saveris, Chen Chen +9
Recent advancements in multimodal models highlight the value of rewritten captions for improving performance, yet key challenges remain. For example, while synthetic captions often…