Joint calibration of Ensemble of Exemplar SVMs

Davide Modolo, Alexander Vezhnevets, Olga Russakovsky, Vittorio Ferrari

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a method for calibrating the Ensemble of Exemplar SVMs model. Unlike the standard approach, which calibrates each SVM independently, our method optimizes their joint performance as an ensemble. We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum. The algorithm dynamically discards parts of the solution space that cannot contain the optimum early on, making the optimization computationally feasible. We experiment with EE-SVM trained on state-of-the-art CNN descriptors. Results on the ILSVRC 2014 and PASCAL VOC 2007 datasets show that (i) our joint calibration procedure outperforms independent calibration on the task of classifying windows as belonging to an object class or not; and (ii) this improved window classifier leads to better performance on the object detection task.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
Publication statusPublished - 2015


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