Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
British Machine Vision Conference
Poselets have been used in a variety of computer vision tasks, such as detection, segmentation, action classification, pose estimation and action recognition, often achieving state-of-the-art performance. Poselet evaluation, however, is computationally intensive as it involves running thousands of scanning window classifiers. We present an algorithm for training a hierarchical cascade of part-based detectors and apply it to speed up poselet evaluation.
Our cascade hierarchy leverages common components shared across poselets. We generate a family of cascade hierarchies, including trees that grow logarithmically on the number of poselet classifiers. Our algorithm, under some reasonable assumptions, finds the optimal tree structure that maximizes speed for a given target detection rate. We test our system on the PASCAL dataset and show an order of magnitude speedup at less than 1% loss in AP.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu
Patrick Lewis, Barlas Oğuz, Wenhan Xiong, Fabio Petroni, Wen-tau Yih, Sebastian Riedel