WALLACE: Weakly Supervised Learning of Deep Convolutional Neural Networks With Multiscale Evidence

This paper presents WALLACE, a new framework of deep convolutional neural networks, which perform ConvNet’s pyramidal feature hierarchy for weakly supervised learning. Most prior works rely on the image pyramid or network ensemble, which is both complicated and usually expensive. Instead, WALLACE is a more simple single-stage network that can predict objects present and location in an image without multiple rescale. Our model is trained efficiently using only global image-level labels, and it could generate meaningful multi-scale semantic feature maps by only one evaluation. Furthermore, a novel constrain-to-highlight loss is proposed to balances region selection among hierarchical feature maps, which additional improve model performance. Extensive experiments on object classification and weakly supervised pointwise object localization show that WALLACE achieves state-of-the-art results on the VOC 2007 and VOC 2012 benchmark without bells and whistles.