内容简介:刚刚过去的一周有很多有意思的工作开源了,比如接吻检测、从步态中进行疾病检测。
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刚刚过去的一周有很多有意思的工作开源了,比如接吻检测、从步态中进行疾病检测。
我们一起来看看吧。
基于CNN的社交网络中室外图像的人类情感分类研究
OutdoorSent: Can Semantic Features Help Deep Learning in Sentiment Analysis of Outdoor Images?
Wyverson B. de Oliveira, Leyza B. Dorini, Rodrigo Minetto, Thiago H. Silva
https://arxiv.org/abs/1906.02331v1
http://dainf.ct.utfpr.edu.br/outdoorsent
直接从两幅图像通过CNN回归单应性矩阵,远远好于之前的SOTA方法
STN-Homography: estimate homography parameters directly
Qiang Zhou, Xin Li
https://arxiv.org/abs/1906.02539v1
好莱坞电影中接吻镜头检测,少儿不宜。。。
Detecting Kissing Scenes in a Database of Hollywood Films
Amir Ziai
https://arxiv.org/abs/1906.01843v1
http://github.com/amirziai/kissing-detector
使用深度学习提取拓扑关系
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
Anirudh Som, Hongjun Choi, Karthikeyan Natesan Ramamurthy, Matthew Buman, Pavan Turaga
https://arxiv.org/abs/1906.01769v1
https://github.com/anirudhsom/PI-Net
跨任务引导注意力模型的一通路多任务模型,用于脑肿瘤分割
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation
Chenhong Zhou, Changxing Ding, Xinchao Wang, Zhentai Lu, Dacheng Tao
https://arxiv.org/abs/1906.01796v1
https://github.com/chenhong-zhou/OM-Net
非监督行人重识别
Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification
Guodong Ding, Salman Khan, Zhenmin Tang, Jian Zhang, Fatih Porikli
https://arxiv.org/abs/1906.01308v1
https://github.com/gddingcs/Dispersion-based-Clustering.git
基于深度学习方法的步态视频中疾病检测
Automatic Health Problem Detection from Gait Videos Using Deep Neural Networks
Rahil Mehrizi, Xi Peng, Shaoting Zhang, Ruisong Liao, Kang Li
https://arxiv.org/abs/1906.01480v1
https://github.com/rmehrizi/multi-view-pose-estimation
BMVC 2017
多视3D目标识别
Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition
Chu Wang, Marcello Pelillo, Kaleem Siddiqi
https://arxiv.org/abs/1906.01592v1
https://github.com/fate3439/dscnn
生成对抗网络综述和术语
Generative Adversarial Networks: A Survey and Taxonomy
Zhengwei Wang, Qi She, Tomas E. Ward
https://arxiv.org/abs/1906.01529v1
https://github.com/sheqi/GAN_Review
评估可缩放的贝叶斯深度学习方法,用于鲁棒计算机视觉
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
https://arxiv.org/abs/1906.01620v1
https://github.com/fregu856/evaluating_bdl
跨域级联深度特征的图像变换方法
Cross-Domain Cascaded Deep Feature Translation
Oren Katzir, Dani Lischinski, Daniel Cohen-Or
https://arxiv.org/abs/1906.01526v1
(将开源,代码未公布)
基于语义的医学图像融合方法
A Semantic-based Medical Image Fusion Approach
Fanda Fan, Yunyou Huang, Lei Wang, Xingwang Xiong, Zihan Jiang, Zhifei Zhang, Jianfeng Zhan
https://arxiv.org/abs/1906.00225v1
https://github.com/fanfanda/Medical-Image-Fusion
区域特定的微分形式的度量映射用于图像配准
Region-specific Diffeomorphic Metric Mapping
Zhengyang Shen, François-Xavier Vialard, Marc Niethammer
https://arxiv.org/abs/1906.00139v1
https://github.com/uncbiag/registration
通过渐进和选择性实例切换进行目标检测的数据增广
Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
Hao Wang, Qilong Wang, Fan Yang, Weiqi Zhang, Wangmeng Zuo
https://arxiv.org/abs/1906.00358v1
https://github.com/Hwang64/PSIS
深度学习边缘检测的对抗样例
Adversarial Examples for Edge Detection: They Exist, and They Transfer
Christian Cosgrove, Alan L. Yuille
https://arxiv.org/abs/1906.00335v1
通过对抗鲁棒学习感知校正表示
Learning Perceptually-Aligned Representations via Adversarial Robustness
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry
https://arxiv.org/abs/1906.00945v1
https://git.io/robust-reps
凝视校正,自监督GAN
GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks
Jichao Zhang, Meng Sun, Jingjing Chen, Hao Tang, Yan Yan, Xueying Qin, Nicu Sebe
https://arxiv.org/abs/1906.00805v1
https://github.com/zhangqianhui/GazeCorrection
深度学习数钢筋的CNN-DC方法
Automated Steel Bar Counting and Center Localization with Convolutional Neural Networks
Zhun Fan, Jiewei Lu, Benzhang Qiu, Tao Jiang, Kang An, Alex Noel Josephraj, Chuliang Wei
https://arxiv.org/abs/1906.00891v1
https://github.com/BenzhangQiu/Steel-bar-Detection
从视频中学习人-物交互的热点方法
Grounded Human-Object Interaction Hotspots from Video (Extended Abstract)
Tushar Nagarajan, Christoph Feichtenhofer, Kristen Grauman
https://arxiv.org/abs/1906.01963v1
http://vision.cs.utexas.edu/projects/interaction-hotspots/
深度学习GAN辅助电子显微镜成像
Partial Scan Electron Microscopy with Deep Learning
Jeffrey M. Ede, Richard Beanland
https://arxiv.org/abs/1905.13667v1
https://github.com/Jeffrey-Ede/partial-STEM
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