CVPR 2019 论文实现代码
【1】《3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans》(CVPR 2019 Oral)
论文链接：https://arxiv.org/abs/1812.07003
GitHub地址：https://github.com/Sekunde/3D-SIS
【2】《Exploiting temporal context for 3D human pose estimation in the wild》(CVPR 2019)
论文链接：https://link.springer.com/chapter/10.1007/978-3-030-01249-6_5
GitHub地址：https://github.com/deepmind/Temporal-3D-Pose-Kinetics
【3】《Zoom To Learn, Learn To Zoom》(CVPR 2019)
论文链接：https://europepmc.org/abstract/med/21513265
GitHub地址：https://github.com/ceciliavision/zoom-learn-zoom
【4】《Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression》(CVPR 2019)
论文链接：https://arxiv.org/abs/1902.09630
GitHub地址：https://github.com/generalized-iou/Detectron.pytorch
【5】《Reliable and Efficient Image Cropping: A Grid Anchor based Approach》(CVPR 2019)
论文地址：https://arxiv.org/abs/1904.04441
GitHub地址：https://github.com/HuiZeng/Grid-Anchor-based-Image-Cropping
【6】《Semantic Image Synthesis with Spatially-Adaptive Normalization Taesung》(CVPR 2019)
论文地址：https://arxiv.org/abs/1903.07291
GitHub地址：https://github.com/divyanshj16/SPADE
【7】《4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks》(CVPR 2019)
论文地址：https://arxiv.org/abs/1904.08755
GitHub地址：https://github.com/StanfordVL/MinkowskiEngine
【8】《Knowledge-Embedded Routing Network for Scene Graph Generation》(CVPR 2019)
论文地址：https://arxiv.org/abs/1903.03326
GitHub地址：https://github.com/yuweihao/KERN
【9】《Deep Flow-Guided Video Inpainting》(CVPR 2019)
论文地址：https://arxiv.org/abs/1905.02884
GitHub地址：https://github.com/nbei/Deep-Flow-Guided-Video-Inpainting
【10】《Convolutional Mesh Regression for Single-Image Human Shape Reconstruction》(CVPR2019)
论文地址：https://arxiv.org/abs/1905.03244
GitHub地址：https://github.com/nkolot/GraphCMR
【11】《Capture, Learning, and Synthesis of 3D Speaking Styles》(CVPR 2019)
论文地址：https://arxiv.org/abs/1905.03079
GitHub地址：https://github.com/TimoBolkart/voca
【12】《Meta-Transfer Learning for Few-Shot Learning》 (CVPR 2019)
论文地址：https://arxiv.org/abs/1812.02391
GitHub地址：https://github.com/y2l/meta-transfer-learning-tensorflow
内容参考链接：https://ai.yanxishe.com/page/postDetail/11399
