Semantically-Adaptive Upsampling for Layout-to-Image Translation
We propose the Semantically-Adaptive UpSampling (SA-UpSample), a general and highly effective upsampling method for the layout-to-image translation task. SA-UpSample has three advantages: 1) Global view. Unlike traditional upsampling methods (e.g., Nearest-neighbor) that only exploit local neighborhoods, SA-UpSample can aggregate semantic information in a global view. 2) Semantically adaptive. Instead of using a fixed kernel for all locations (e.g., Deconvolution), SA-UpSample enables semantic class-specific upsampling via generating adaptive kernels for different locations. 3) Efficient. Unlike Spatial Attention which uses a fully-connected strategy to connect all the pixels, SA-UpSample only considers the most relevant pixels, introducing little computational overhead. We observe that SA-UpSample achieves consistent and substantial gains on six popular datasets. The source code will be made publicly available.
Code (0)
등록된 구현이 없습니다.
Tasks
TranslationSimilar Papers 제목 키워드 기반
Layout-to-Image Translation with Double Pooling Generative Adversarial Networks
In this paper, we address the task of layout-to-image translation, which aims to translate an input semantic layout to a realistic image. One open challenge widely observed in existing methods is the lack of effective se…
TranslationGeometry Aligned Variational Transformer for Image-conditioned Layout Generation
Layout generation is a novel task in computer vision, which combines the challenges in both object localization and aesthetic appraisal, widely used in advertisements, posters, and slides design. An accurate and pleasant…
Layout DesignLayout GenerationObject LocalizationBabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation
As global cross-lingual communication intensifies, language barriers in visually rich documents such as PDFs remain a practical bottleneck. Existing document translation pipelines face a tension between linguistic proces…
Attribution Upsampling should Redistribute, Not Interpolate
Attribution methods in explainable AI rely on upsampling techniques that were designed for natural images, not saliency maps. Standard bilinear and bicubic interpolation systematically corrupts attribution signals throug…
Dual-layer Image Compression via Adaptive Downsampling and Spatially Varying Upconversion
Ultra high resolution (UHR) images are almost always downsampled to fit small displays of mobile end devices and upsampled to its original resolution when exhibited on very high-resolution displays. This observation moti…
DecoderImage Compression