Scribble-Supervised Semantic Segmentation Inference

Abstract

In this paper, we propose a progressive segmentation inference (PSI) framework to tackle with scribble-supervised semantic segmentation. In virtue of latent contextual dependency, we encapsulate two crucial cues, contextual pattern propagation and semantic label diffusion, to enhance and refine pixel-level segmentation results from partially known seeds. In contextual pattern propagation, different-granular contextual patterns are correlated and leveraged to properly diffuse pattern information based on graphical model, so as to increase the inference confidence of pixel label prediction. Further, depending on high confidence scores of estimated pixels, the initial annotated seeds are progressively spread over the image through dynamically learning an adaptive decision strategy. The two cues are finally modularized to form a close-looping update process during pixel-wise label inference. Extensive experiments demonstrate that our proposed progressive segmentation inference can benefit from the combination of spatial and semantic context cues, and meantime achieve the state-of-the-art performance on two public scribble segmentation datasets.