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重磅干货,第一时间送达
本文转载自我爱计算机视觉,禁二次转载
刚刚过去的一周含五一假期,工作日第一天,CV君汇总了过去一周计算机视觉领域新出的开源代码,涉及到自动驾驶目标检测、医学图像分割、风格迁移、神经架构搜索、图卷积神经网络、网络减枝、语义分割、目标跟踪等,含多篇CVPR 2019的论文的代码实现。
可能是大部分华人在放假吧,感觉开源工程比之前少一点。
希望对你有帮助~
ICIP 2019
用于自动驾驶汽车目标检测的雷达区域候选网络
RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles
Ramin Nabati, Hairong Qi
http://arxiv.org/abs/1905.00526v1
https://github.com/mrnabati/RRPN
用于自动驾驶汽车感知的精确的合成图像与LiDAR数据集
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
Braden Hurl, Krzysztof Czarnecki, Steven Waslander
https://arxiv.org/abs/1905.00160v1
https://uwaterloo.ca/waterloo-intelligent-systems-engineering-lab/projects/precise-synthetic-image-and-lidar-presil-dataset-autonomous
CT医学图像重建高级框架PYRO-NN,构建于TensorFlow之上
PYRO-NN: Python Reconstruction Operators in Neural Networks
Christopher Syben, Markus Michen, Bernhard Stimpel, Stephan Seitz, Stefan Ploner, Andreas K. Maier
https://arxiv.org/abs/1904.13342v1
https://github.com/csyben/PYRO-NN
跨图像库检测未知但相同目标类
Learning to Find Common Objects Across Image Collections
Amirreza Shaban, Amir Rahimi, Stephen Gould, Byron Boots, Richard Hartley
https://arxiv.org/abs/1904.12936v1
https://github.com/haamoon/finding_common_object
CVPR 2019
风格迁移
Style Transfer by Relaxed Optimal Transport and Self-Similarity
Nicholas Kolkin, Jason Salavon, Greg Shakhnarovich
https://arxiv.org/abs/1904.12785v1
https://github.com/nkolkin13/STROTSS
神经架构搜索
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
Xin Chen, Lingxi Xie, Jun Wu, Qi Tian
https://arxiv.org/abs/1904.12760v1
https://github.com/chenxin061/pdarts
图卷积神经网络,点云的非监督特征学习
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural Network
Ling Zhang, Zhigang Zhu
https://arxiv.org/abs/1904.12359v1
https://github.com/lingzhang1/ContrastNet
网络压缩,滤波剪枝
LeGR: Filter Pruning via Learned Global Ranking
Ting-Wu Chin, Ruizhou Ding, Cha Zhang, Diana Marculescu
https://arxiv.org/abs/1904.12368v1
https://github.com/cmu-enyac/LeGR
高分辨率网络用于逼真风格迁移
这里的高分辨率网络就是CV君曾经介绍过的:
分割、检测与定位,高分辨率网络显神威!这会是席卷深度学习的通用结构吗?
技术进步真的好快啊!
High-Resolution Network for Photorealistic Style Transfer
Ming Li, Chunyang Ye, Wei Li
https://arxiv.org/abs/1904.11617v1
https://github.com/limingcv/Photorealistic-Style-Transfer
CVPR 2019 Oral
神经架构搜索自动设计语义分割网络Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan Yuille, Li Fei-Fei
https://arxiv.org/abs/1901.02985
https://github.com/tensorflow/models/tree/master/research/deeplab (官方)
https://github.com/MenghaoGuo/AutoDeeplab (非官方)
CVPR 2019 Oral
目前最强的目标跟踪算法SiamRPN++开源实现
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, Junjie Yan
https://arxiv.org/abs/1812.11703
https://github.com/PengBoXiangShang/SiamRPN_plus_plus_PyTorch (非官方)
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