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

OpenCV边缘检测

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import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('1024.jpg',0)
edges = cv2.Canny(img,100,200)

plt.subplot(121),plt.imshow(img,cmap='gray')
plt.title('original'),plt.xticks([]),plt.yticks([])
plt.subplot(122),plt.imshow(edges,cmap='gray')
plt.title('edge'),plt.xticks([]),plt.yticks([])

plt.show()