计算机视觉开源周报20191003期
总结了过去一周CV领域的最新开源代码，发现本周出现多份很有价值的高质量、重量级工作，比如致力于使得图卷积网络更深的DeepGCNs、Mask引导的注意力网络大大改进了遮挡行人重识别、格灵深瞳轻量级人脸识别比赛冠军模型VarGFaceNet、比LSTM更优的新RNN模型IndRNN、还有异常强大的字符级文本识别CharNet。
一种web运行的半自动图像标注的灵活框架LOST（Label Objects and Save Time）
LOST: A flexible framework for semi-automatic image annotation
Jonas Jäger, Gereon Reus, Joachim Denzler, Viviane Wolff, Klaus Fricke-Neuderth
https://arxiv.org/abs/1910.07486v1
https://github.com/l3p-cv/lost对抗表示学习中的全局最优化问题
On the Global Optima of Kernelized Adversarial Representation Learning
Bashir Sadeghi, Runyi Yu, Vishnu Naresh Boddeti
ICCV 2019
https://arxiv.org/abs/1910.07423v1
https://github.com/human-analysis/Kernel-ARL学习泛化的全尺度表示，用于人员重识别，模型更小，精度更优
Learning Generalisable Omni-Scale Representations for Person Re-Identification
Kaiyang Zhou, Xiatian Zhu, Yongxin Yang, Andrea Cavallaro, Tao Xiang
ICCV 2019
https://arxiv.org/abs/1910.06827v1
https://github.com/KaiyangZhou/deep-person-reid将ResNet和DenseNet引入到图卷积网络中，可以训练更深（达112层）的GCN，在多个任务中达到了更高的精度。
DeepGCNs: Making GCNs Go as Deep as CNNs
Guohao Li, Matthias Müller, Guocheng Qian, Itzel C. Delgadillo, Abdulellah Abualshour, Ali Thabet, Bernard Ghanem
ICCV 2019
https://arxiv.org/abs/1910.06849v1
https://github.com/lightaime/deep_gcns_torch
https://github.com/lightaime/deep_gcns训练智能体玩“躲猫猫”游戏
Visual Hide and Seek
Boyuan Chen, Shuran Song, Hod Lipson, Carl Vondrick
https://arxiv.org/abs/1910.07882v1
http://www.cs.columbia.edu/~bchen/visualhideseek/掩膜引导的注意力网络，用于遮挡严重的行人检测，在多个数据集实现了更高的最好精度。CityPersons提升9.5%，Caltech提升5.0%。
Mask-Guided Attention Network for Occluded Pedestrian Detection
Yanwei Pang, Jin Xie, Muhammad Haris Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, Ling Shao
ICCV 2019
https://arxiv.org/abs/1910.06160v2
https://github.com/Leotju/MGAN一种几何启发的卷积操作，有效提升了消失点检测
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
Yichao Zhou, Haozhi Qi, Jingwei Huang, Yi Ma
https://arxiv.org/abs/1910.06316v1
https://github.com/zhou13/neurvps单次神经架构搜索，基于自我评估模版网络，在CIFAR和ImageNet数据集达到最先进的性能
One-Shot Neural Architecture Search via Self-Evaluated Template Network
Xuanyi Dong, Yi Yang
ICCV 2019
https://arxiv.org/abs/1910.05733v1
https://github.com/D-X-Y/NAS-Projects学习鉴别特征，用于非监督域适应
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
Seungmin Lee, Dongwan Kim, Namil Kim, Seong-Gyun Jeong
ICCV 2019
https://arxiv.org/abs/1910.05562v1
https://github.com/postBG/DTA.pytorch可变组卷积神经网络，可以支持大规模人脸识别，同时减少计算成本和参数。获得格灵深瞳轻量级人脸识别挑战赛冠军！
VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition
Mengjia Yan, Mengao Zhao, Zining Xu, Qian Zhang, Guoli Wang, Zhizhong Su
ICCV 2019 Workshop
https://arxiv.org/abs/1910.04985v1
https://github.com/zma-c-137/VarGFaceNet发明一种称为Hadamard乘积的递归连接，构建了独立递归神经网络（IndRNN），其中同一层中的神经元彼此独立并且跨层连接。
IndRNN可有效替代LSTM，精度更高的同时，速度是其10倍！
Deep Independently Recurrent Neural Network (IndRNN)
Shuai Li, Wanqing Li, Chris Cook, Yanbo Gao, Ce Zhu
https://arxiv.org/abs/1910.06251v1
https://github.com/Sunnydreamrain/IndRNN_pytorch一种以字符为基本单元的单阶段文本检测识别网络，在三个标准基准上对CharNet结果显示，其结果以最先进的结果大大领先之前的算法，比如ICDAR 2015上从65.33％改进到71.08％，TotalText上从54.0％跃升至69.23％。
Convolutional Character Networks
Linjie Xing, Zhi Tian, Weilin Huang, Matthew R. Scott
ICCV 2019
https://arxiv.org/abs/1910.07954v1
https://github.com/MalongTech/research-charnet基于语音指令实现的自动驾驶
Conditional Driving from Natural Language Instructions
Junha Roh, Chris Paxton, Andrzej Pronobis, Ali Farhadi, Dieter Fox
CoRL 2019
https://arxiv.org/abs/1910.07615v1
https://sites.google.com/view/language-grounded-driving医学图像域适应 | 提出了一种新型的无监督域自适应框架，称为协作特征集合自适应（CFEA），改进了眼底图像分割的精度
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
Peng Liu, Bin Kong, Zhongyu Li, Shaoting Zhang, Ruogu Fang
MICCAI 2019
https://arxiv.org/abs/1910.07638v1
https://github.com/cswin/AWC
