366 citations · 411 across the 9 of their papers we have counts for
9 papers
Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results
Wang Fang, Shirin Rahimi, Olivia Bennett +5
Point-cloud semantic segmentation underpins a wide range of critical applications. Although recent deep architectures and large-scale datasets have driven impressive closed-set per…
Batch Group Normalization
Xiao-Yun Zhou, Jiacheng Sun, Nanyang Ye +6
Deep Convolutional Neural Networks (DCNNs) are hard and time-consuming to train. Normalization is one of the effective solutions. Among previous normalization methods, Batch Normal…
Boosting Few-Shot Learning With Adaptive Margin Loss
Aoxue Li, Weiran Huang, Xu Lan +3
Few-shot learning (FSL) has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in learning to generalize from a few examples. T…
Universal Person Re-Identification
Xu Lan, Xiatian Zhu, Shaogang Gong
Most state-of-the-art person re-identification (re-id) methods depend on supervised model learning with a large set of cross-view identity labelled training data. Even worse, such…
Self-Referenced Deep Learning
Xu Lan, Xiatian Zhu, Shaogang Gong
Knowledge distillation is an effective approach to transferring knowledge from a teacher neural network to a student target network for satisfying the low-memory and fast running r…
Collaborative Deep Learning Across Multiple Data Centers
Kele Xu, Haibo Mi, Dawei Feng +4
Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter da…
Person Search by Multi-Scale Matching
Xu Lan, Xiatian Zhu, Shaogang Gong
We consider the problem of person search in unconstrained scene images. Existing methods usually focus on improving the person detection accuracy to mitigate negative effects impos…
Knowledge Distillation by On-the-Fly Native Ensemble
Xu Lan, Xiatian Zhu, Shaogang Gong
Knowledge distillation is effective to train small and generalisable network models for meeting the low-memory and fast running requirements. Existing offline distillation methods…
Deep Reinforcement Learning Attention Selection for Person Re-Identification
Xu Lan, Hanxiao Wang, Shaogang Gong +1
Existing person re-identification (re-id) methods assume the provision of accurately cropped person bounding boxes with minimum background noise, mostly by manually cropping. This…