2 citations · 3 across the 12 of their papers we have counts for
5 papers · 1 filter
Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion
Yueming Pan, Ruoyu Feng, Qi Dai +5
Latent Diffusion Models (LDMs) inherently follow a coarse-to-fine generation process, where high-level semantic structure is generated slightly earlier than fine-grained texture. T…
Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation
Xiaoyu Yue, Zidong Wang, Yuqing Wang +5
Recent studies have demonstrated the importance of high-quality visual representations in image generation and have highlighted the limitations of generative models in image unders…
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning
Chengqi Duan, Rongyao Fang, Yuqing Wang +5
Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that specify multiple objects with precise…
SARTM: Segment Any RGB Thermal Model with Language aided Distillation
Dong Xing, Jinhe Zhang, Hang Yang +1
The recent Segment Anything Model (SAM) demonstrates strong instance segmentation performance across various downstream tasks. However, SAM is trained solely on RGB data, limiting…
Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation
Yuqing Wang, Zhijie Lin, Yao Teng +4
Autoregressive visual generation models typically rely on tokenizers to compress images into tokens that can be predicted sequentially. A fundamental dilemma exists in token repres…