7 papers · 1 filter
Optimized Gradient Clipping for Noisy Label Learning
Xichen Ye, Yifan Wu, Weizhong Zhang +3
Previous research has shown that constraining the gradient of loss function with respect to model-predicted probabilities can enhance the model robustness against noisy labels. The…
MagicFace: Training-free Universal-Style Human Image Customized Synthesis
Yibin Wang, Weizhong Zhang, Cheng Jin
Current human image customization methods leverage Stable Diffusion (SD) for its rich semantic prior. However, since SD is not specifically designed for human-oriented generation,…
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation
Zhibo Zhang, Ximing Yang, Weizhong Zhang +1
Cross-modal knowledge transfer enhances point cloud representation learning in LiDAR semantic segmentation. Despite its potential, the \textit{weak teacher challenge} arises due to…
DreamText: High Fidelity Scene Text Synthesis
Yibin Wang, Weizhong Zhang, Honghui Xu +1
Scene text synthesis involves rendering specified texts onto arbitrary images. Current methods typically formulate this task in an end-to-end manner but lack effective character-le…
Robust Fine-tuning for Pre-trained 3D Point Cloud Models
Zhibo Zhang, Ximing Yang, Weizhong Zhang +1
This paper presents a robust fine-tuning method designed for pre-trained 3D point cloud models, to enhance feature robustness in downstream fine-tuned models. We highlight the limi…
PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering
Yibin Wang, Weizhong Zhang, Jianwei Zheng +1
Image composition involves seamlessly integrating given objects into a specific visual context. Current training-free methods rely on composing attention weights from several sampl…
Out-of-Distribution Detection using Neural Activation Prior
Weilin Wan, Weizhong Zhang, Quan Zhou +2
Out-of-distribution detection (OOD) is a crucial technique for deploying machine learning models in the real world to handle the unseen scenarios. In this paper, we first propose a…