Computer Vision - ECCV 2020 Workshops

W24 - Advances in Image Manipulation Workshop and Challenges.- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results.- Residual Feature Distillation Network for Lightweight Image Super-Resolution. -Efficient Image Super-Resolution using Pixel Attention.- LarvaNet: Hierarchical Super-Resolution via Multi-exit Architecture.- Efficient Super-Resolution using MobileNetV3.- Multi-Attention Based Ultra Lightweight Image Super-Resolution.- Adaptive Hybrid Composition based Super-Resolution Network via Fine-grained Channel Pruning.- IdleSR: E cient Super-Resolution Network with Multi-Scale IdleBlocks.- IM 2020 Challenge on Learned Image Signal Processing Pipeline.- EEDNet: Enhanced Encoder Decoder Network for AutoISP.- AWNet: Attentive Wavelet Network for ImageISP.- PyNET-CA: Enhanced PyNET with Channel Attention for End-to-end Mobile Image Signal Processing.- AIM 2020 Challenge on Rendering Realistic Bokeh.- BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh.- Bokeh Rendering from Defocus Estimation.- Human Motion Transfer from Poses in the Wild.- FamilyGAN: Generating Kin Face Images using Generative Adversarial Networks.- Genetic-GAN: Synthesizing images between two domains by genetic crossover.- GIA-Net: Global Information Aware Network for Low-light Imaging.- Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network.- A Benchmark for Burst Color Constancy.- Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration.- AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results.- Real Image Super Resolution via Heterogeneous Model Ensemble using GP-NAS.- Enhanced Adaptive Dense Connection Single Image Super-Resolution.- Self-Calibrated Attention Neural Network for Real-World Super Resolution.- FAN: Frequency Aggregation Network for Real Image Super-resolution.- Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image