7 citations · 10 across the 6 of their papers we have counts for
6 papers
IBCapsNet: Information Bottleneck Capsule Network for Noise-Robust Representation Learning
Canqun Xiang, Chen Yang, Jiaoyan Zhao
Capsule networks (CapsNets) are superior at modeling hierarchical spatial relationships but suffer from two critical limitations: high computational cost due to iterative dynamic r…
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving
Zhennan Wang, Jianing Teng, Canqun Xiang +4
While end-to-end autonomous driving has advanced significantly, prevailing methods remain fundamentally misaligned with human cognitive principles in both perception and planning.…
ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification
Jinping Wang, Xiaojun Tan, Jianhuang Lai +2
Previous studies have shown the great potential of capsule networks for the spatial contextual feature extraction from {hyperspectral images (HSIs)}. However, the sampling location…
DPR-CAE: Capsule Autoencoder with Dynamic Part Representation for Image Parsing
Canqun Xiang, Zhennan Wang, Wenbin Zou +1
Parsing an image into a hierarchy of objects, parts, and relations is important and also challenging in many computer vision tasks. This paper proposes a simple and effective capsu…
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles
Zhennan Wang, Canqun Xiang, Wenbin Zou +1
The strong correlation between neurons or filters can significantly weaken the generalization ability of neural networks. Inspired by the well-known Tammes problem, we propose a no…
SAIS: Single-stage Anchor-free Instance Segmentation
Canqun Xiang, Shishun Tian, Wenbin Zou +1
In this paper, we propose a simple yet efficientinstance segmentation approach based on the single-stage anchor-free detector, termed SAIS. In our approach, the instancesegmentatio…