We propose a geometry constrained network, termed GC-Net, for weakly
supervised object localization (WSOL). GC-Net consists of three modules: a
detector, a generator and a classifier. The detector predicts the object
location defined by a set of coefficients describing a geometric shape (i.e.
ellipse or rectangle), which is geometrically constrained by the mask produced
by the generator. The classifier takes the resulting masked images as input and
performs two complementary classification tasks for the object and background.
To make the mask more compact and more complete, we propose a novel multi-task
loss function that takes into account area of the geometric shape, the
categorical cross-entropy and the negative entropy. In contrast to previous
approaches, GC-Net is trained end-to-end and predict object location without
any post-processing (e.g. thresholding) that may require additional tuning.
Extensive experiments on the CUB-200-2011 and ILSVRC2012 datasets show that
GC-Net outperforms state-of-the-art methods by a large margin. Our source code
is available at this https URL

本文提出了一种名为 GC-Net 的几何约束网络，用于弱监督物体定位，并引入了一种新颖的多任务损失函数，使得该网络可以无需后处理（如阈值）即可进行端到端训练，并在 CUB-200-2011 数据集上表现卓越。