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

We propose a novel edge guided generative adversarial network with contrastive learning (ECGAN) for the challenging semantic image synthesis task. Although considerable improvements have been achieved by the community in the recent period, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic labels do not provide detailed structural information, making it challenging to synthesize local details and structures; 2) The widely adopted CNN operations such as convolution, down-sampling, and normalization usually cause spatial resolution loss and thus cannot fully preserve the original semantic information, leading to semantically inconsistent results (e.g., missing small objects); 3) Existing semantic image synthesis methods focus on modeling “local” semantic information from a single input semantic layout. However, they ignore “global” semantic information of multiple input semantic layouts, i.e., semantic cross-relations between pixels across different input layouts. To tackle 1), we propose to use the edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module. Edge information is produced by a convolutional generator and introduces detailed structure information. To tackle 2), we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout to preserve the semantic information. To tackle 3), inspired by current methods in contrastive learning, we propose a novel contrastive learning method, which aims to enforce pixel embeddings belonging to the same semantic class to generate more similar image content than those from different classes. We further propose a novel multi-scale contrastive learning method that aims to push same-class features from different scales closer together being able to capture more semantic relations by explicitly exploring the structures of labeled pixels from multiple input semantic layouts from different scales. Experiments on three challenging datasets show that our methods achieve significantly better results than state-of-the-art approaches.

I. Introduction

Semantic image synthesis refers to generating photo-realistic images conditioned on pixel-level semantic labels. This task has a wide range of applications such as image editing and content generation [1], [2], [3], [4], [5]. Although existing methods conducted interesting explorations, we still observe unsatisfactory aspects, mainly in the generated local structures and details, as well as small-scale objects, which we believe are mainly due to three reasons: 1) Conventional methods [4], [6], [7] generally take the semantic label map as input directly. However, the input label map provides only structural information between different semantic-class regions and does not contain any structural information within each semantic-class region, making it difficult to synthesize rich local structures within each class. Taking label map $S$ in Fig. 1 as an example, the generator does not have enough structural guidance to produce a realistic bed, window, and curtain from only the input label ($S$). 2) The classic deep network architectures are constructed by stacking convolutional, down-sampling, normalization, non-linearity, and up-sampling layers, which will cause the problem of spatial resolution losses of the input semantic labels. 3) Existing methods for this task are typically based on global image-level generation. In other words, they accept a semantic layout containing several object classes and aim to generate the appearance of each one using the same network. In this way, all the classes are treated equally. However, because different semantic classes have distinct properties, using specified network learning for each would intuitively facilitate the complex generation of multiple classes.