4 citations · 9 across the 5 of their papers we have counts for
5 papers
Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning
Xiaoshuang Chen, Zhongyi Sun, Ke Yan +2
Class Incremental Learning (CIL) aims to handle the scenario where data of novel classes occur continuously and sequentially. The model should recognize the sequential novel classe…
Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting
Xiaoshuang Chen, Hongtao Lu
Fully-supervised crowd counting is a laborious task due to the large amounts of annotations. Few works focus on weekly-supervised crowd counting, where only the global crowd number…
PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting
Xiaoshuang Chen, Yiru Zhao, Yu Qin +4
Crowd counting aims to learn the crowd density distributions and estimate the number of objects (e.g. persons) in images. The perspective effect, which significantly influences the…
Semi-supervised Learning with Contrastive Predicative Coding
Jiaxing Wang, Yin Zheng, Xiaoshuang Chen +2
Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural netwo…
RaFM: Rank-Aware Factorization Machines
Xiaoshuang Chen, Yin Zheng, Jiaxing Wang +2
Factorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation. Different from existing FM-based approaches which use a fixed rank…