Image semantic segmentation contains two sub-tasks, segmenting and labeling. However, the recent fully convolutional network (FCN) based methods often ignore the first sub-task and consider it as a direct labeling one. Even though these methods have achieved competitive performances, they obtained spatially fragmented and disconnected outputs. The reason is that, pixel-level relationships inside the deepest layers become inconsistent since traditional FCNs do not have any explicit pixel grouping mechanism. To address this problem, a multi-step supervised learning method, which contains image-level supervised learning step and pixel-level supervised learning step, is proposed. Specifically, as for the visualized result of image semantic segmentation, it is actually an image-to-image transformation problem, from RGB domain to category label domain. The recent conditional generative adversarial network (cGAN) has achieved significant performance for image-to-image generation task, and the generated image remains good regional connectivity. Therefore, a cGAN supervised by RGB-category label map is used to obtain a coarse segmentation mask, which avoids generating disconnected segmentation results to a certain extent. Furthermore, an interaction information (II) loss term is proposed for cGAN to remain the spatial structure of the segmentation mask. Additionally, dilated convolutional networks (DCNs) have achieved significant performance in object detection field, especially for small objects because of its special receptive field settings. Specific to image semantic segmentation, if each pixel is seen as an object, this task can be transformed to object detection. In this case, combined with the segmentation mask from cGAN, a DCN supervised by the pixel-level label is used to finalize the category recognition of each pixel in the image. The proposed method achieves satisfactory performances on three public and challenging datasets for image semantic segmentation. ? Springer Nature Switzerland AG 2018.