Context Prior based Semantic-Spatial Graph Network for Human Parsing

Abstract

Recently, many methods enlarged the kernel size or fused multi-scale features to capture contextual information for parsing the hard samples, such as heavy occlusions, limb absence, and complex poses. Nevertheless, those required higher computational and cannot explore the effective global context to achieve more precise parsing results. In this paper, we propose an end-to-end network for human parsing, named Context Prior based Semantic-Spatial Graph Network (CP-SSGNet), which achieves higher precision and consistency by encoding the context prior in the graph model. CP-SSGNet consists of three modules, Semantic Constraint Module (SCM), Spatial Perceiving Module (SPM), and Intra-class Attention Module (IAM). SCM captures richer global dependencies by encoding semantic structure context prior in a semantic graph, which can reduce semantic structure errors. SPM learns enhanced local features by encoding the spatial consistency context prior in the spatial graph, which can optimize boundaries and reduce the local consistency errors. IAM utilizes the spatial graph with strong–weak connections for intra-class features aggregation to distinguish the inter-class features clearly. Extensive experiments are conducted on two challenging datasets, PASCAL-Person-Part and LIP, effectively achieving state-of-the-arts performance.