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Scene parsing from images is a fundamental yet challenging problem in visualcontent understanding. In this dense prediction task, the parsing model assignsevery pixel to a categorical label, which requires the contextual informationof adjacent image patches. So the challenge for this learning task is tosimultaneously describe the geometric and semantic properties of objects or ascene. In this paper, we explore the effective use of multi-layer featureoutputs of the deep parsing networks for spatial-semantic consistency bydesigning a novel feature aggregation module to generate the appropriate globalrepresentation prior, to improve the discriminative power of features. Theproposed module can auto-select the intermediate visual features to correlatethe spatial and semantic information. At the same time, the multiple skipconnections form a strong supervision, making the deep parsing network easy totrain. Extensive experiments on four public scene parsing datasets prove thatthe deep parsing network equipped with the proposed feature aggregation modulecan achieve very promising results.

Multi-layer Feature Aggregation for Deep Scene Parsing Models