text: Object localization is an important computer vision problem with a variety of applications. The lack of large scale object-level annotations and the relative abundance of image-level labels makes a compelling case for weak supervision in the object localization task. Deep Convolutional Neural Networks are a class of state-of-the-art methods for the related problem of object recognition. In this paper, we describe a novel object localization algorithm which uses classification networks trained on only image labels. This weakly supervised method leverages local spatial and semantic patterns captured in the convolutional layers of classification networks. We propose an efficient beam search based approach to detect and localize multiple objects in images. The proposed method significantly outperforms the state-of-the-art in standard object localization data-sets with a 8 point increase in mAP scores.
  @cite_19: Successful methods for visual object recognition typically rely on training datasets containing lots of richly annotated images. Detailed image annotation, e.g. by object bounding boxes, however, is both expensive and often subjective. We describe a weakly supervised convolutional neural network (CNN) for object classification that relies only on image-level labels, yet can learn from cluttered scenes containing multiple objects. We quantify its object classification and object location prediction performance on the Pascal VOC 2012 (20 object classes) and the much larger Microsoft COCO (80 object classes) datasets. We find that the network (i) outputs accurate image-level labels, (ii) predicts approximate locations (but not extents) of objects, and (iii) performs comparably to its fully-supervised counterparts using object bounding box annotation for training.
summary: Oquab et.al. @cite_19 has proposed a weakly supervised object localization system which learns from training samples with objects in composite scenes by explicitly searching over candidate object locations and scales during the training phase. While this method performs well on data-sets with complex scenes, the extent of localization is limited with respect to estimating one point in the test image. The extent of the object is not estimated and detecting multiple instances of the same object class is not considered. In our proposed approach, we estimate both the location and extent of objects and are capable of estimating multiple instances of objects in the test image. Also, we use pre-existing classification networks for localization where as @cite_19 proposes training custom adaptation layers.