Multi-Granular Semantic Mining for Weakly Supervised Semantic Segmentation

This paper solves the problem of learning image semantic segmentation using image-level supervision. The task is promising in terms of reducing annotation efforts, yet extremely challenging due to the difficulty to directly associate high-level concepts with low-level appearance. While current efforts handle each concept independently, we take a broader perspective to harvest implicit, holistic structures of semantic concepts, which express valuable prior knowledge for accurate concept grounding. This raises multi-granular semantic mining, a new formalism allowing flexible specification of complex relations in the label space. In particular, we propose a heterogeneous graph neural network (Hgnn) to model the heterogeneity of multi-granular semantics within a set of input images. The Hgnn consists of two types of sub-graphs: 1) an external graph characterizes the relations across different images to mine inter-image contexts; and for each image, 2) an internal graph is constructed to mine inter-class semantic dependencies within each individual image. Through heterogeneous graph learning, our Hgnn is able to land a comprehensive understanding of object patterns, leading to more accurate semantic concept grounding. Extensive experimental results show that Hgnn outperforms the current state-of-the-art approaches on the popular PASCAL VOC 2012 and COCO 2014 benchmarks. Our code is available at: https://github.com/maeve07/HGNN.git.