CV Code | 计算机视觉开源周报20190904期
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本图来自第14篇论文
本文总结了过去一周的开源计算机视觉相关代码，有好几篇来自顶会NeurIPS 2019、ICCV 2019 等。
涉及方向众多，包括模型迁移、实时目标检测、目标预测、去雨质量评价、人体运动迁移、知识蒸馏、强化学习等。
通过属性映射研究深度模型知识的可迁移性
Deep Model Transferability from Attribution Maps
Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song
NeurIPS 2019
https://arxiv.org/abs/1909.11902v1
https://github.com/zju-vipa/TransferbilityFromAttributionMaps
多目标位置预测
Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments
Olly Styles, Tanaya Guha, Victor Sanchez
WACV 2020
https://arxiv.org/abs/1909.11944v1
https://github.com/olly-styles/Multiple-Object-Forecasting
对真实下雨图像的去雨质量评价，主观和客观方法
Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image
Qingbo Wu, Lei Wang, King N. Ngan, Hongliang Li, Fanman Meng
https://arxiv.org/abs/1909.11983v1
https://github.com/wqb-uestc
人体运动模仿、表观迁移和新视图合成的统一框架
Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis
Wen Liu, Zhixin Piao, Jie Min, Wenhan Luo, Lin Ma, Shenghua Gao
ICCV 2019
https://arxiv.org/abs/1909.12224v1
https://svip-lab.github.io/project/impersonator.html
基于信息多重蒸馏网络的轻量图像超分辨率
Lightweight Image Super-Resolution with Information Multi-distillation Network
Zheng Hui, Xinbo Gao, Yunchu Yang, Xiumei Wang
ACM Multimedia 2019
https://arxiv.org/abs/1909.11856v1
https://github.com/Zheng222/IMDN
隐式语义数据增强，提高了ResNets 和 DenseNets 等网络在各种数据集比如 CIFAR-10, CIFAR-100 and ImageNet上的泛化性.
Implicit Semantic Data Augmentation for Deep Networks
Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Cheng Wu, Gao Huang
NeurIPS 2019
https://arxiv.org/abs/1909.12220v1
https://github.com/blackfeather-wang/ISDA-for-Deep-Networks
针对CNN的降低内存使用的压缩感知训练方法
CAT: Compression-Aware Training for bandwidth reduction
Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson
https://arxiv.org/abs/1909.11481v1
https://github.com/CAT-teams/CAT
知识蒸馏的“无师自通”方法
Revisit Knowledge Distillation: a Teacher-free Framework
Li Yuan, Francis E.H.Tay, Guilin Li, Tao Wang, Jiashi Feng
https://arxiv.org/abs/1909.11723v1
https://github.com/yuanli2333/Teacher-free-Knowledge-Distillation
多步视觉任务的强化学习
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks via Reward Shaping
Andrew Hundt, Benjamin Killeen, Heeyeon Kwon, Chris Paxton, Gregory D. Hager
https://arxiv.org/abs/1909.11730v1
https://github.com/jhu-lcsr/good_robot
统一的视觉语言预训练，针对图像描述与问答
Unified Vision-Language Pre-Training for Image Captioning and VQA
Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J. Corso, Jianfeng Gao
https://arxiv.org/abs/1909.11059v1
https://github.com/LuoweiZhou/VLP
PolSAR图像分类
PolSAR Image Classification Based on Dilated Convolution and Pixel-Refining Parallel Mapping network in the Complex Domain
Xiao Dongling, Liu Chang
https://arxiv.org/abs/1909.10783v1
https://github.com/PROoshio/CRPM-Net
联合人头部和身体关系学习的检测方法
Relational Learning for Joint Head and Human Detection
Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou
https://arxiv.org/abs/1909.10674v1
（代码将开源，还未公布地址）
巧合的是，本周还有另一篇来自旷视的人头和身体结合的行人检测方法：
Double Anchor R-CNN for Human Detection in a Crowd
Kevin Zhang, Feng Xiong, Peize Sun, Li Hu, Boxun Li, Gang Yu
https://arxiv.org/abs/1909.09998v1
快速精确的卷积目标检测，用于实时嵌入式平台
Fast and Accurate Convolutional Object Detectors for Real-time Embedded Platforms
Min-Kook Choi, Jaehyung Park, Heechul Jung, Jinhee Lee, Soo-Heang Eo
https://arxiv.org/abs/1909.10798v1
https://github.com/mkchoi-0323/modified_refinedet
用于目标检测平衡训练的生成正样本包围框的方法
Generating Positive Bounding Boxes for Balanced Training of Object Detectors
Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan
https://arxiv.org/abs/1909.09777v1
（代码将开源，还未公布地址）
3D点云上进行有效图卷积的球形卷积核
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
Huan Lei, Naveed Akhtar, Ajmal Mian
https://arxiv.org/abs/1909.09287v1
https://github.com/hlei-ziyan/SPH3D-GCN
从文本图像中提取实体
EATEN: Entity-aware Attention for Single Shot Visual Text Extraction
He guo, Xiameng Qin, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding
https://arxiv.org/abs/1909.09380v1
https://github.com/beacandler/EATEN
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