Now showing items 1-8 of 8

    • Ensemble of multiple instance classifiers for image re-ranking 

      Sener F.; Ikizler-Cinbis, N. (Elsevier Ltd, 2014)
      Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ...
    • Görsel arama sonuçlarının çoklu örnekle öğrenme yöntemiyle yeniden sıralanması 

      Şener, Fadime; Cinbiş, N. I.; Duygulu-Şahin, Pınar (IEEE, 2012-04)
      Bu çalışmada, çoklu öğrenme yöntemi ile metin tabanlı arama motorlarından elde edilen görsel sorgu sonuçlarını iyileştirmek için geliştirilmiş olan, zayıf denetimli öğrenen bir yöntem sunulmaktadır. Bu yöntemde arama ...
    • Multi-channel TDMA scheduling in wireless sensor networks 

      Uyanık, Özge (Bilkent University, 2013)
      The Multiple Instance Learning (MIL) paradigm arises to be useful in many application domains, whereas it is particularly suitable for computer vision problems due to the difficulty of obtaining manual labeling. Multiple ...
    • On recognizing actions in still images via multiple features 

      Şener, Fadime; Bas, C.; Ikizler-Cinbis, N. (Springer, Berlin, Heidelberg, 2012)
      We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly ...
    • Recognizing human actions from noisy videos via multiple instance learning 

      şener, Fadime; Samet, Nermin; Duygulu, Pınar; Ikizler-Cinbis, N. (IEEE, 2013)
      In this work, we study the task of recognizing human actions from noisy videos and effects of noise to recognition performance and propose a possible solution. Datasets available in computer vision literature are relatively ...
    • Two-person interaction recognition via spatial multiple instance embedding 

      Sener F.; Ikizler-Cinbis, N. (Academic Press Inc., 2015)
      Abstract In this work, we look into the problem of recognizing two-person interactions in videos. Our method integrates multiple visual features in a weakly supervised manner by utilizing an embedding-based multiple instance ...
    • Utilizing multiple instance learning for computer vision tasks 

      Şener, Fadime (Bilkent University, 2013)
      The Multiple Instance Learning (MIL) paradigm arises to be useful in many application domains, whereas it is particularly suitable for computer vision problems due to the difficulty of obtaining manual labeling. Multiple ...
    • Weakly supervised object localization with multi-fold multiple instance learning 

      Cinbis, R. G.; Verbeek, J.; Schmid, C. (IEEE Computer Society, 2017)
      Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly ...