* Some of the PhD/Master/Undergraduate degree theses of the students in the Lab.


  • “Deep Structural Models for Fine-grained Visual Parsing”, Xiaodan Liang, 2016  PDF
  • “The Semantic Knowledge Embedded Deep Representation Learning and Its Applications on Visual Understanding”, Ruimao Zhang, 2017 PDF
  • “From Supervised to Self-driven : Learning with Big Visual Data for High Level Semantic Understanding”, Keze Wang, 2017 PDF
  • “Knowledge Representation Embedded Visual Reasoning: Models and Applications”, Tianshui Chen, 2018 PDF


  • “A Novel And-Or Graph Learning Method for Recognizing Objects with Large Appearance Variance”, Xiaolong Wang, 2014 PDF
  • “Efficient Non-Linear Filtering and Saliency Detection for Image Analysis”, Keyang Shi, 2014 PDF
  • “Person Re-identification by Matching Compositional Template with Cluster Sampling”, Yuanlu Xu, 2014 PDF
  • “Is Fast R-CNN Doing Well for Pedestrian Detection?”, Liliang Zhang, 2016 PDF
  • “Hierarchical Long Short-term Memory for Geometric Scene Parsing “, Zhanglin Peng, 2016 PDF
  • “Deep Deconvolution Network for Face Hallucination”, Yukai Shi, 2016 PDF
  • “Geometric Knowledge fused Recurrent Neural Network: a novel Approach for 3D Human Pose Estimation”, MuDe Lin, 2018 PDF
  • “Research on Deep Attentional Mechanism and It’s Applications on High-Level Large-Scale Image Understanding”, Zhouxia Wang, 2018 PDF
  • “Research On Person Image Analysis and Understanding: Person Search and Image Synthesis”, Bochao Wang, 2018 PDF
  • “Research on Hierarchical Semantic Embedding and Its Application on Fine-Grained Image Recognition “, Wenxi Wu, 2018 PDF


  • “Face Alignment and Detection in Unconstrained Environment”, Yuan Xie, 2016 PDF
  • “Self-Learning Framework for Visual Recognition”, Xiaopeng Yan, 2017 PDF
  • “Crowd Analysis and Exploration using Deep Recurrent Spatial-Aware Network”, Hongjun Wang, 2018 PDF