50 citations · 89 across the 15 of their papers we have counts for
15 papers
Monocular Depth Estimation via Neural Network with Learnable Algebraic Group and Ring Structures
Qianlei Wang, Kexun Chen, Shaolin Zhang +3
Monocular depth estimation (MDE) has witnessed remarkable progress driven by Convolutional Neural Networks and transformer-based architectures. However, these approaches typically…
Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers
Longkun Zou, Wanru Zhu, Ke Chen +4
Semantic pattern of an object point cloud is determined by its topological configuration of local geometries. Learning discriminative representations can be challenging due to larg…
ShuffleMix: Improving Representations via Channel-Wise Shuffle of Interpolated Hidden States
Kangjun Liu, Ke Chen, Lihua Guo +2
Mixup style data augmentation algorithms have been widely adopted in various tasks as implicit network regularization on representation learning to improve model generalization, wh…
Improving Deep Representation Learning via Auxiliary Learnable Target Coding
Kangjun Liu, Ke Chen, Kui Jia +1
Deep representation learning is a subfield of machine learning that focuses on learning meaningful and useful representations of data through deep neural networks. However, existin…
Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose
Yichen Zhang, Jiehong Lin, Ke Chen +3
Domain gap between synthetic and real data in visual regression (e.g. 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse c…
Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization
Kangjun Liu, Ke Chen, Kui Jia
Fine-grained visual classification can be addressed by deep representation learning under supervision of manually pre-defined targets (e.g., one-hot or the Hadamard codes). Such ta…