Zlin’s Extraordinary Research Oasis

Zeal, Excellence, Reliability and Openness

Welcome to the ZERO Lab, the research group lead by Prof. Zhouchen Lin (Zlin), affiliated to School of Electronics Engineering and Computer Science, Peking University. We research on machine learning and computer vision.

Recruiting

Recentest Publications

Quickly discover relevant content by filtering publications.

On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent. JMLR, 2020.

Our study aims to give the convergence rate analysis of the primal solutions for the accelerated randomized dual coordinate ascent.

Spatial Pyramid Based Graph Reasoning for Semantic Segmentation. CVPR, 2020.

The convolution operation suffers from a limited receptive filed, while global modeling is fundamental to dense prediction tasks, such …

Unified Graph and Low-rank Tensor Learning for Multi-view Clustering. AAAI, 2020.

We propose a novel framework to jointly learn the affinity graph and low-rank tensor decomposition for multi-view clustering.

Synthetic Depth Transfer for Monocular 3D Object Pose Estimation in the Wild. AAAI, 2020.

Monocular object pose estimation is an important yet challenging computer vision problem. Depth features can provide useful information …

Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families. AAAI, 2020.

Inspired from the dynamical systems, this study aims to unravel and improve ResNets.

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. AAAI, 2020.

We propose a novel training algorithm to improve the generalization performance of GCNs on graphs with few labeled nodes.

SOGNet: Scene Overlap Graph Network for Panoptic Segmentation. AAAI, 2020.

Our study aims to explicitly predict overlap relations and resolve overlaps in a differentiable way for the panoptic output.

Accelerated Alternating Direction Method of Multipliers:An Optimal O(1/K) Nonergodic Analysis. JSC, 2019.

The Alternating Direction Method of Multipliers (ADMM) is widely used for linearly constrained convex problems. It is proven to have an …

Contact

  • Wechat account