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Computer Vision – ECCV 2018 Workshops

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Table of Contents

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    Book Overview
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    Chapter 1 Multi–scale Recursive and Perception–Distortion Controllable Image Super–Resolution
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    Chapter 2 Bi-GANs-ST for Perceptual Image Super-Resolution
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    Chapter 3 Multi-modal Spectral Image Super-Resolution
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    Chapter 4 Generative Adversarial Network-Based Image Super-Resolution Using Perceptual Content Losses
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    Chapter 5 ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
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    Chapter 6 The Unreasonable Effectiveness of Texture Transfer for Single Image Super-Resolution
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    Chapter 7 Perception-Enhanced Image Super-Resolution via Relativistic Generative Adversarial Networks
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    Chapter 8 Analyzing Perception-Distortion Tradeoff Using Enhanced Perceptual Super-Resolution Network
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    Chapter 9 Scale-Recurrent Multi-residual Dense Network for Image Super-Resolution
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    Chapter 10 Deep Networks for Image-to-Image Translation with Mux and Demux Layers
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    Chapter 11 CARN: Convolutional Anchored Regression Network for Fast and Accurate Single Image Super-Resolution
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    Chapter 12 Multiple Connected Residual Network for Image Enhancement on Smartphones
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    Chapter 13 Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones
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    Chapter 14 Deep Residual Attention Network for Spectral Image Super-Resolution
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    Chapter 15 Range Scaling Global U-Net for Perceptual Image Enhancement on Mobile Devices
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    Chapter 16 Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks
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    Chapter 17 Fast Perceptual Image Enhancement
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    Chapter 18 PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study
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    Chapter 19 AI Benchmark: Running Deep Neural Networks on Android Smartphones
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    Chapter 20 PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
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    Chapter 21 The 2018 PIRM Challenge on Perceptual Image Super-Resolution
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    Chapter 22 PIRM2018 Challenge on Spectral Image Super-Resolution: Methods and Results
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    Chapter 23 MAM: Transfer Learning for Fully Automatic Video Annotation and Specialized Detector Creation
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    Chapter 24 Leveraging Uncertainty to Rethink Loss Functions and Evaluation Measures for Egocentric Action Anticipation
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    Chapter 25 PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks
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    Chapter 26 MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-Streams
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    Chapter 27 VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results
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    Chapter 28 VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results
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    Chapter 29 VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results
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    Chapter 30 An End-to-End Tree Based Approach for Instance Segmentation
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    Chapter 31 Self-supervised Segmentation by Grouping Optical-Flow
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    Chapter 32 Motion Selectivity of Neurons in Self-driving Networks
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    Chapter 33 Imitation Learning of Path-Planned Driving Using Disparity-Depth Images
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    Chapter 34 Removal of Visual Disruption Caused by Rain Using Cycle-Consistent Generative Adversarial Networks
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    Chapter 35 Real-Time Dynamic Object Detection for Autonomous Driving Using Prior 3D-Maps
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    Chapter 36 Learning Driving Behaviors for Automated Cars in Unstructured Environments
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    Chapter 37 Multichannel Semantic Segmentation with Unsupervised Domain Adaptation
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    Chapter 38 Driving Data Collection Framework Using Low Cost Hardware
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    Chapter 39 3D Bounding Boxes for Road Vehicles: A One-Stage, Localization Prioritized Approach Using Single Monocular Images
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    Chapter 40 MASON: A Model AgnoStic ObjectNess Framework
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    Chapter 41 Object Detection at 200 Frames per Second
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    Chapter 42 Motion Segmentation Using Spectral Clustering on Indian Road Scenes
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    Chapter 43 Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding
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    Chapter 44 Localisation via Deep Imagination: Learn the Features Not the Map
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    Chapter 45 Interest Point Detectors Stability Evaluation on ApolloScape Dataset
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    Chapter 46 Reliable Multilane Detection and Classification by Utilizing CNN as a Regression Network
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Computer Vision – ECCV 2018 Workshops
Published by
Springer International Publishing, March 2019
DOI 10.1007/978-3-030-11021-5
978-3-03-011020-8, 978-3-03-011021-5

Leal-Taixé, Laura, Roth, Stefan

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 33%
Student > Bachelor 5 15%
Student > Ph. D. Student 5 15%
Student > Doctoral Student 2 6%
Lecturer > Senior Lecturer 1 3%
Other 3 9%
Unknown 6 18%
Readers by discipline Count As %
Computer Science 13 39%
Engineering 9 27%
Mathematics 2 6%
Neuroscience 1 3%
Arts and Humanities 1 3%
Other 0 0%
Unknown 7 21%