时间：3月14日上午10:30分
地点：生物电子学国家重点实验室三楼会议室
学术报告题目：
Title:
Boosting methods for predicting structured output variables.
Abstract:
Boosting is a method for learning a single accurate predictor by linearly
combining a set of less accurate weak learners. Recently, structured learning has
found many applications in computer vision. Thus far it has not been clear how one can train
a boosting model that is directly optimized for predicting multivariate or structured outputs.
To bridge this gap, inspired by structured support vector machines (SSVM), here we propose a boosting
algorithm for structured output prediction, which we refer to as StructBoost. StructBoost supports nonlinear
structured learning by combining a set of weak structured learners. As SSVM generalizes SVM, our StructBoost
generalizes standard boosting approaches such as AdaBoost, or LPBoost to structured learning. The resulting
optimization problem of StructBoost is more challenging than SSVM in the sense that it may involve exponentially many
variables and constraints. In contrast, for SSVM one usually has an exponential number of constraints and a
cutting-plane method is used. In order to efficiently solve StructBoost, we formulate an equivalent 1-slack
formulation and solve it using a combination of cutting planes and column generation. We show the versatility
and usefulness of StructBoost on a range of problems such as optimizing the tree loss for hierarchical multi-class
classification, optimizing the Pascal overlap criterion for robust visual tracking and learning conditional random
field parameters for image segmentation.
Speaker:
Chunhua Shen is an Associate Professor at School of Computer Science, University of Adelaide.
Prior to that, he was with the computer vision program at NICTA(National ICT Australia),
Canberra Research Laboratory for 5.5 years. His research interests are in the intersection of computer vision
and statistical machine learning. Recent work has been on real-time object detection, large-scale image retrieval
and classification, and scalable nonlinear optimization.
He studied at Nanjing University, at Australian National University, and received his PhD degree from the University of Adelaide. From 2012 to 2016, he holds an Australian Research Council Future Fellowship.