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The key to integrating visual language tasks is to establish a good alignmentstrategy. Recently, visual semantic representation has achieved fine-grainedvisual understanding by dividing grids or image patches. However, thecoarse-grained semantic interactions in image space should not be ignored,which hinders the extraction of complex contextual semantic relations at thescene boundaries. This paper proposes superpixels as comprehensive and robustvisual primitives, which mine coarse-grained semantic interactions byclustering perceptually similar pixels, speeding up the subsequent processingof primitives. To capture superpixel-level semantic features, we propose aMultiscale Difference Graph Convolutional Network (MDGCN). It allows parsingthe entire image as a fine-to-coarse visual hierarchy. To reason actualsemantic relations, we reduce potential noise interference by aggregatingdifference information between adjacent graph nodes. Finally, we propose amulti-level fusion rule in a bottom-up manner to avoid understanding deviationby mining complementary spatial information at different levels. Experimentsshow that the proposed method can effectively promote the learning of multipledownstream tasks. Encouragingly, our method outperforms previous methods on allmetrics. Our code will be released upon publication.

Superpixel Semantics Representation and Pre-training for Vision-Language Task