36 citations · 49 across the 5 of their papers we have counts for
5 papers
Adaptive Data Augmentation for Contrastive Learning
Yuhan Zhang, He Zhu, Shan Yu
In computer vision, contrastive learning is the most advanced unsupervised learning framework. Yet most previous methods simply apply fixed composition of data augmentations to imp…
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions
He Zhu, Xihua Li, Xuemin Zhao +2
Recently, more and more people study online for the convenience of access to massive learning materials (e.g. test questions/notes), thus accurately understanding learning material…
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
Jin Gao, Yan Lu, Xiaojuan Qi +5
Tracking visual objects from a single initial exemplar in the testing phase has been broadly cast as a one-/few-shot problem, i.e., one-shot learning for initial adaptation and few…
Intra-Class Uncertainty Loss Function for Classification
He Zhu, Shan Yu
Most classification models can be considered as the process of matching templates. However, when intra-class uncertainty/variability is not considered, especially for datasets cont…
Learning Gaussian Instance Segmentation in Point Clouds
Shih-Hung Liu, Shang-Yi Yu, Shao-Chi Wu +2
This paper presents a novel method for instance segmentation of 3D point clouds. The proposed method is called Gaussian Instance Center Network (GICN), which can approximate the di…