Titlebook: Computer Vision – ECCV 2018 Workshops; Munich, Germany, Sep Laura Leal-Taixé,Stefan Roth Conference proceedings 2019 Springer Nature Switze

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CRAFT: Complementary Recommendation by Adversarial Feature Transformcomplementary recommendation. Our model learns a non-linear transformation between the two manifolds of source and target item categories (e.g., tops and bottoms in outfits). Given a large dataset of images containing instances of co-occurring items, we train a generative transformer network directl

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Full-Body High-Resolution Anime Generation with Progressive Structure-Conditional Generative Adversacharacter images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resolution images. However, existing approaches have limitations in achieving both high image quality and structural consistency at the

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Convolutional Photomosaic Generation via Multi-scale Perceptual Lossesof the mosaic collectively resemble a perceptually plausible image. In this paper, we consider the challenge of automatically generating a photomosaic from an input image. Although computer-generated photomosaicking has existed for quite some time, none have considered simultaneously exploiting colo

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Joint Future Semantic and Instance Segmentation Predictionntly introduced towards better machine intelligence. However, predicting directly in the image color space seems an overly complex task, and predicting higher level representations using semantic or instance segmentation approaches were shown to be more accurate. In this work, we introduce a novel p