Object Localization based on Structural SVM using Privileged Information
We propose a structured prediction algorithm for object localization based on Support Vector Machines (SVMs) using privileged information. Privileged information provides useful high-level knowledge for image understanding and facilitates learning a reliable model even with a small number of training examples. In our setting, we assume that such information is available only at training time since it may be difficult to obtain from visual data accurately without human supervision. Our goal is to improve performance by incorporating privileged information into ordinary learning framework and adjusting model parameters for better generalization. We tackle object localization problem based on a novel structural SVM using privileged information, where an alternating loss-augmented inference procedure is employed to handle the term in the objective function corresponding to privileged information. We apply the proposed algorithm to the Caltech-UCSD Birds 200-2011 dataset, and obtain encouraging results suggesting further investigation into the benefit of privileged information in structured prediction.
- The method is a fairly direct generalization of the framework of learning with privileged information to the structured output case. The method is effective for the object localization task and results in good improvements in localization accuracy. - Does this formulation of SSVM+ reduce to SVM+ for binary classification with the right choice of loss function and feature vector, just as SSVM reduce to SVM in the classical case? It looks like the authors' formulation of SSVM+ contains separate slack variables \xi_i for each example x_i and there are extra degrees of freedom. - The loss functions \Delta and \Delta^* seems to be the same in the object localization task, but it is not stated clearly in section 3.3. - How effective is the alternating inference procedure between y and y^*? How many alternating iterations are required? When the parameter vectors w and w^* are far from the optimal solution, could this alternating inference procedure get stuck in bad local minima? - I am interested in knowing the strength of the privileged information encoded in the object localization task. If we train a regular SSVM with the privileged information and also use the privileged information during testing, how well will the algorithm do? This can give us some understanding on how informative the privileged features are when they're available during test time, vs only during training time under the LUPI framework. - Overall I think the method is a simple extension of the learning under privileged information framework, but it works well. It can be improved if the authors can give more explanations of their formulation and how it links to the case of binary SVM+, or give some theoretical justification like those under the LUPI framework.