4 citations · 6 across the 4 of their papers we have counts for
7 papers · 1 filter
Relax Within, Balance Across: Geometry-Guided Load Balancing for Vision-Language Mixture-of-Experts
Ziang Wu, Peng Jin, Qishen Yin +4
Vision-language MoE batches contain different numbers of image and text tokens. Image resolution, image count, tiling, and prompt length all change this token mix. We call the stan…
ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?
Bin Zhu, Yanhao Jia, Kexin Zhao +12
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in open-world reasoning and understanding. However, a critical ambiguity pe…
WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
Yuwei Niu, Munan Ning, Mengren Zheng +9
Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evaluation standards predominantly focus on…
CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step
Zheyuan Liu, Munan Ning, Qihui Zhang +8
Current text-to-image (T2I) generation models struggle to align spatial composition with the input text, especially in complex scenes. Even layout-based approaches yield suboptimal…
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation
Qihui Zhang, Munan Ning, Zheyuan Liu +7
Multimodal Large Language Models (MLLMs) have emerged to tackle the challenges of Visual Question Answering (VQA), sparking a new research focus on conducting objective evaluations…
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
Bin Lin, Zhenyu Tang, Yang Ye +7
Recent advances demonstrate that scaling Large Vision-Language Models (LVLMs) effectively improves downstream task performances. However, existing scaling methods enable all model…