Weakly Supervised Object Detection with Symmetry Context

Abstract

Recently, weakly supervised object detection (WSOD) with image-level annotation has attracted great attention in the field of computer vision. The problem is often formulated as multiple instance learning in the existing studies, which are often trapped by discriminative object parts and fail to localize the object boundary precisely. In this work, we alleviate this problem by exploiting contextual information that may potentially increase object localization accuracy. Specifically, we propose novel context proposal mining strategies and a Symmetry Context Module to leverage surrounding contextual information of precomputed region proposals. Both naive and Gaussian-based context proposal mining methods are adopted to yield informative context proposals symmetrically surrounding region proposals. Then mined context proposals are fed into our Symmetry Context Module to encourage the model to select proposals that contain the whole object, rather than the most discriminative object parts. Experimental results show that the mean Average Precision (mAP) of the proposed method achieves 52.4% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-art methods and demonstrating its effectiveness for weakly supervised object detection.