41 citations · 49 across the 7 of their papers we have counts for
7 papers
Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance
Dazhong Shen, Guanglu Song, Zeyue Xue +2
Classifier-Free Guidance (CFG) has been widely used in text-to-image diffusion models, where the CFG scale is introduced to control the strength of text guidance on the whole image…
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
Lingting Zhu, Zeyue Xue, Zhenchao Jin +4
Cross-modality medical image synthesis is a critical topic and has the potential to facilitate numerous applications in the medical imaging field. Despite recent successes in deep-…
Align, Adapt and Inject: Sound-guided Unified Image Generation
Yue Yang, Kaipeng Zhang, Yuying Ge +4
Text-guided image generation has witnessed unprecedented progress due to the development of diffusion models. Beyond text and image, sound is a vital element within the sphere of h…
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
Zeyue Xue, Guanglu Song, Qiushan Guo +4
Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images,…
Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction
Zhuofan Zong, Dongzhi Jiang, Guanglu Song +4
In this paper, we propose a new paradigm, named Historical Object Prediction (HoP) for multi-view 3D detection to leverage temporal information more effectively. The HoP approach i…
Large-batch Optimization for Dense Visual Predictions
Zeyue Xue, Jianming Liang, Guanglu Song +4
Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tac…
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach
Zeyue Xue, Shuang Luo, Chao Wu +3
Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying o…