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We propose a novel ECGAN for the challenging semantic image synthesis task.Although considerable improvements have been achieved by the community in therecent period, the quality of synthesized images is far from satisfactory dueto three largely unresolved challenges. 1) The semantic labels do not providedetailed structural information, making it challenging to synthesize localdetails and structures; 2) The widely adopted CNN operations such asconvolution, down-sampling, and normalization usually cause spatial resolutionloss and thus cannot fully preserve the original semantic information, leadingto semantically inconsistent results (e.g., missing small objects); 3) Existingsemantic image synthesis methods focus on modeling 'local' semantic informationfrom a single input semantic layout. However, they ignore 'global' semanticinformation of multiple input semantic layouts, i.e., semantic cross-relationsbetween pixels across different input layouts. To tackle 1), we propose to usethe edge as an intermediate representation which is further adopted to guideimage generation via a proposed attention guided edge transfer module. Totackle 2), we design an effective module to selectively highlightclass-dependent feature maps according to the original semantic layout topreserve the semantic information. To tackle 3), inspired by current methods incontrastive learning, we propose a novel contrastive learning method, whichaims to enforce pixel embeddings belonging to the same semantic class togenerate more similar image content than those from different classes. Wefurther propose a novel multi-scale contrastive learning method that aims topush same-class features from different scales closer together being able tocapture more semantic relations by explicitly exploring the structures oflabeled pixels from multiple input semantic layouts from different scales.

Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis