Spatial division networks for weakly supervised detection

With only global image-level annotations, weakly supervised learning of deep convolutional neural networks has shown enough capacity in classification and localization but lack of ability to present the detection explicitly. In this work, we propose a novel spatial division network, which is applied to detect bounding boxes only with weak supervision. The essence of our model is two innovative differentiable modules, determination network and parameterized division, which perform the spatial division in feature maps of classification networks. After training, the learned parameters of the spatial division would correspond to a set of predicted bounding box coordinates. To demonstrate the effectiveness of our model for multi-label classification and weakly supervised detection, we conduct extensive experiments on the multi-MNIST dataset. Experimental results show our spatial division networks can (1) help improve the accuracy of multi-label classification, (2) implement in an end-to-end way only with the image-level annotations, and (3) output accurate bounding box coordinate, thereby achieving multi-digits detection.