Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

Ramazan Gokberk Cinbis 1 Jakob Verbeek 1 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : 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 supervised learning. In this case, the supervised information is restricted to binary labels that indicate the absence/presence of object instances in the image, without their locations. We follow a multiple-instance learning approach that iteratively trains the detector and infers the object locations in the positive training images. Our main contribution is a multi-fold multiple instance learning procedure, which prevents training from prematurely locking onto erroneous object locations. This procedure is particularly important when using high-dimensional representations, such as Fisher vectors and convolutional neural network features. We also propose a window refinement method, which improves the localization accuracy by incorporating an objectness prior. We present a detailed experimental evaluation using the PASCAL VOC 2007 dataset, which verifies the effectiveness of our approach.
Document type :
Journal articles
Complete list of metadatas

Contributor : Thoth Team <>
Submitted on : Thursday, September 3, 2015 - 9:32:41 AM
Last modification on : Friday, February 8, 2019 - 2:50:00 PM
Long-term archiving on: Friday, December 4, 2015 - 10:39:57 AM


Files produced by the author(s)



Ramazan Gokberk Cinbis, Jakob Verbeek, Cordelia Schmid. Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2016. ⟨hal-01123482v2⟩



Record views


Files downloads