4 citations · 10 across the 25 of their papers we have counts for
7 papers · 2 filters
One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models
Mingzhao Yang, Shangchao Su, Bin Li +1
In recent years, One-shot Federated Learning methods based on Diffusion Models have garnered increasing attention due to their remarkable performance. However, most of these method…
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions
Teng Fu, Xiaocong Wang, Haiyang Yu +3
Multiple object tracking (MOT) tends to become more challenging when severe occlusions occur. In this paper, we analyze the limitations of traditional Convolutional Neural Network-…
Chinese Text Recognition with A Pre-Trained CLIP-Like Model Through Image-IDS Aligning
Haiyang Yu, Xiaocong Wang, Bin Li +1
Scene text recognition has been studied for decades due to its broad applications. However, despite Chinese characters possessing different characteristics from Latin characters, s…
Orientation-Independent Chinese Text Recognition in Scene Images
Haiyang Yu, Xiaocong Wang, Bin Li +1
Scene text recognition (STR) has attracted much attention due to its broad applications. The previous works pay more attention to dealing with the recognition of Latin text images…
Collaborative Chinese Text Recognition with Personalized Federated Learning
Shangchao Su, Haiyang Yu, Bin Li +1
In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organi…
Weakly-Supervised Text Instance Segmentation
Xinyan Zu, Haiyang Yu, Bin Li +1
Text segmentation is a challenging vision task with many downstream applications. Current text segmentation methods require pixel-level annotations, which are expensive in the cost…
Uncertainty-aware U-Net for Medical Landmark Detection
Ziyang Ye, Haiyang Yu, Bin Li
Heatmap-based methods play an important role in anatomical landmark detection. However, most current heatmap-based methods assume that the distributions of all landmarks are the sa…