5 papers
Let ViT Speak: Generative Language-Image Pre-training
Yan Fang, Mengcheng Lan, Zilong Huang +7
In this paper, we present \textbf{Gen}erative \textbf{L}anguage-\textbf{I}mage \textbf{P}re-training (GenLIP), a minimalist generative pretraining framework for Vision Transformers…
iVGR: Internalizing Visually Grounded Reasoning for MLLMs with Reinforcement Learning
Chang-Bin Zhang, Yujie Zhong, Qiang Zhang +1
While visually grounded Chain-of-Thought (CoT) has emerged as a promising paradigm to enhance fine-grained perception in multimodal large language models (MLLMs), its efficacy duri…
TextSculptor: Training and Benchmarking Scene Text Editing
Yiheng Lin, Siyu Jiao, Xiaohan Lan +12
Recent advances in Multimodal Large Language Models (MLLMs) and diffusion-based generative models have substantially improved prompt-driven image editing. However, scene text editi…
Mr. DETR++: Instructive Multi-Route Training for Detection Transformers with Mixture-of-Experts
Chang-Bin Zhang, Yujie Zhong, Kai Han
Existing methods enhance the training of detection transformers by incorporating an auxiliary one-to-many assignment. In this work, we treat the model as a multi-task framework, si…
v-CLR: View-Consistent Learning for Open-World Instance Segmentation
Chang-Bin Zhang, Jinhong Ni, Yujie Zhong +1
In this paper, we address the challenging problem of open-world instance segmentation. Existing works have shown that vanilla visual networks are biased toward learning appearance…