Learning semantic segmentation models under image-level supervision is far more challenging than under fully supervised setting. Without knowing the exact pixel-label correspondence, most weakly-supervised methods rely on external models to infer pseudo pixel-level labels for training semantic segmentation models. In this paper, we aim to develop a single neural network without resorting to any external models. We propose a novel self-guided strategy to fully utilize features learned across multiple levels to progressively generate the dense pseudo labels. First, we use high-level features as class-specific localization maps to roughly locate the classes. Next, we propose an affinity-guided method to encourage each localization map to be consistent with their intermediate level features. Third, we adopt the training image itself as guidance and propose a self-guided refinement to further transfer the image's inherent structure into the maps. Finally, we derive pseudo pixel-level labels from these localization maps and use the pseudo labels as ground truth to train the semantic segmentation model. Our proposed self-guided strategy is a unified framework, which is built on a single network and alternatively updates the feature representation and refines localization maps during the training procedure. Experimental results on PASCAL VOC 2012 segmentation benchmark demonstrate that our method outperforms other weakly-supervised methods under the same setting.
Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of objectness priors to generate foreground background masks. Unfortunately these priors either require training pixel-level annotations bounding boxes, or still yield inaccurate object boundaries. Here, we propose a novel method to extract markedly more accurate masks from the pre-trained network itself, forgoing external objectness modules. This is accomplished using the activations of the higher-level convolutional layers, smoothed by a dense CRF. We demonstrate that our method, based on these masks and a weakly-supervised loss, outperforms the state-of-the-art tag-based weakly-supervised semantic segmentation techniques. Furthermore, we introduce a new form of inexpensive weak supervision yielding an additional accuracy boost. In this paper, we deal with a weakly supervised semantic segmentation problem where only training images with image-level labels are available. We propose a weakly supervised semantic segmentation method which is based on CNN-based class-specific saliency maps and fully-connected CRF. To obtain distinct class-specific saliency maps which can be used as unary potentials of CRF, we propose a novel method to estimate class saliency maps which improves the method proposed by (2014) significantly by the following improvements: (1) using CNN derivatives with respect to feature maps of the intermediate convolutional layers with up-sampling instead of an input image; (2) subtracting the saliency maps of the other classes from the saliency maps of the target class to differentiate target objects from other objects; (3) aggregating multiple-scale class saliency maps to compensate lower resolution of the feature maps. After obtaining distinct class saliency maps, we apply fully-connected CRF by using the class maps as unary potentials. By the experiments, we show that the proposed method has outperformed state-of-the-art results with the PASCAL VOC 2012 dataset under the weakly-supervised setting.