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Semantic image segmentation is the process of assigning semantically relevant labels to all pixels in an image. Hierarchical Conditional Random Fields (HCRFs) are a popular and successful approach this problem. One reason for their popularity is their ability to incorporate contextual information at different scales. However, existing HCRF models do not allow multiple labels to be assigned to individual nodes. At higher scales in the image, this results in an oversimplified model, since multiple classes can be reasonable expected to appear within a single region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Furthermore, neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales.