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Scene parsing is an indispensable component in understanding the semanticswithin a scene. Traditional methods rely on handcrafted local features andprobabilistic graphical models to incorporate local and global cues. Recently,methods based on fully convolutional neural networks have achieved new recordson scene parsing. An important strategy common to these methods is theaggregation of hierarchical features yielded by a deep convolutional neuralnetwork. However, typical algorithms usually aggregate hierarchicalconvolutional features via concatenation or linear combination, which cannotsufficiently exploit the diversities of contextual information in multi-scalefeatures and the spatial inhomogeneity of a scene. In this paper, we propose amixture-of-experts scene parsing network (MoE-SPNet) that incorporates aconvolutional mixture-of-experts layer to assess the importance of featuresfrom different levels and at different spatial locations. In addition, wepropose a variant of mixture-of-experts called the adaptive hierarchicalfeature aggregation (AHFA) mechanism which can be incorporated into existingscene parsing networks that use skip-connections to fuse features layer-wisely.In the proposed networks, different levels of features at each spatial locationare adaptively re-weighted according to the local structure and surroundingcontextual information before aggregation. We demonstrate the effectiveness ofthe proposed methods on two scene parsing datasets including PASCAL VOC 2012and SceneParse150 based on two kinds of baseline models FCN-8s andDeepLab-ASPP.

MoE-SPNet: A Mixture-of-Experts Scene Parsing Network