Awesome

RESAIL⛵: Retrieval-based Spatially Adaptive Normalization for Semantic Image Synthesis (CVPR2022)

Abstract: Semantic image synthesis is a challenging task with many practical applications. Albeit remarkable progress has been made in semantic image synthesis with spatially- adaptive normalization, existing methods usually normal- ize the feature activations under the coarse-level guidance (e.g., semantic class). However, different parts of a seman- tic object (e.g., wheel and window of car) are quite differ- ent in structures and textures, making blurry synthesis re- sults usually inevitable due to the missing of fine-grained guidance. In this paper, we propose a novel normaliza- tion module, termed as REtrieval-based Spatially Adap- tIve normaLization (RESAIL), for introducing pixel level fine-grained guidance to the normalization architecture. Specifically, we first present a retrieval paradigm by find- ing a content patch of the same semantic class from train- ing set with the most similar shape to each test seman- tic mask. Then, the retrieved patches are composited into retrieval-based guidance, which can be used by RESAIL for pixel level fine-grained modulation on feature activations, thereby greatly mitigating blurry synthesis results. More- over, distorted ground-truth images are also utilized as al- ternatives of retrieval-based guidance for feature normal- ization, further benefiting model training and improving vi- sual quality of generated images. Experiments on several challenging datasets show that our RESAIL performs favor- ably against state-of-the-arts in terms of quantitative met- rics, visual quality, and subjective evaluation.

Paper in arxiv.org

• ADE20K.
  • Download official dataset named ADEChallengeData2016.zip and unzip this file at the corresponding directory, i.e. unzip ADEChallengeData2016.zip.
  • Download official instance annotations named annotations_instance.tar and extract annotation files at ADE20K dataset root, i.e. tar xvf annotations_instance.tar -C ADEChallengeData2016/ (in the situation where both ADEChallengeData2016.zip and annotations_instance.tar are downloaded at the same directory).
  • Directories are organized as follows:

ADEChallengeData2016/
    ├── annotations/
    │   ├── training/
    │   │   ├── ADE_train_00000001.png
    │   │   ├── ADE_train_00004043.png
    │   │   ├── ...
    │   │   └── ADE_train_00020210.png
    │   └── validation/
    │       ├── ADE_val_00000001.png
    │       ├── ADE_val_00000401.png
    │       ├── ...
    │       └── ADE_val_00002000.png
    ├── annotations_instance/
    │   ├── training/
    │   │   ├── ADE_train_00000001.png
    │   │   ├── ADE_train_00004043.png
    │   │   ├── ...
    │   │   └── ADE_train_00020210.png
    │   └── validation/
    │       ├── ADE_val_00000001.png
    │       ├── ADE_val_00000401.png
    │       ├── ...
    │       └── ADE_val_00002000.png
    │── images/
    │    ├── training/
    │    │   ├── ADE_train_00000001.jpg
    │    │   ├── ADE_train_00004043.jpg
    │    │   ├── ...
    │    │   └── ADE_train_00020210.jpg
    │    └── validation/
    │       ├── ADE_val_00000001.jpg
    │       ├── ADE_val_00000401.jpg
    │       ├── ...
    │       └── ADE_val_00002000.jpg
    ├── objectInfo150.txt
    └── sceneCategories.txt

todo model's downlongding, preparation of cityscape and brief of train & test