Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Structured learning has found many applications in computer vision recently. Analogues to structured support vector machines (SSVM), here we propose boosting algorithms for predicting multivariate or structured outputs, which is referred to as StructBoost. As SSVM generalizes SVM, our StructBoost generalizes standard boosting such as AdaBoost, or LPBoost to structured learning. AdaBoost, LPBoost and many other conventional boosting methods arise as special cases of StructBoost. The resulting optimization problem of StructBoost is more challenging than SSVM in the sense that the problem of StructBoost can 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 propose 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 few problems such as hierarchical multi-class classification, robust visual tracking and image segmentation. In particular, we train a tracking-by-detection based object tracker using the proposed structured boosting. Tracking is implemented as structured output prediction by maximizing the Pascal image area overlap criterion. We show that the structural tracker not only significantly outperforms conventional classification based trackers that do not directly optimize the Pascal image overlap criterion, but also outperforms many other state-of-the-art trackers on the tested videos.

StructBoost: Boosting Methods for Predicting Structured Output Variables