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RSS订阅原创 论文笔记-Multimodal Unsupervised Image-to-Image Translation
论文信息 论文标题：Multimodal Unsupervised Image-to-Image Translation 论文出处：ECCV 2018 论文作者：Xun Huang, Ming-Yu Liu, Serge J. Belongie, Jan Kautz 研究机构：康奈尔大学；英伟达 代码链接：https://github.com/nvlabs/MUNIT 引用信息： @inproceedings{DBLP:conf/eccv/HuangLBK18, autho
2020-10-02 23:53:37
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原创 论文笔记-Unsupervised Domain Adaptation via Regularized Conditional Alignment
论文信息 论文标题：Unsupervised Domain Adaptation via Regularized Conditional Alignment 论文作者：Safa Cicek, Stefano Soatto 研究机构：UCLA Vision Lab; University of California, Los Angeles 论文出处：ICCV 2019 引用信息： @inproceedings{DBLP:conf/iccv/CicekS19, author =
2020-09-28 23:24:16
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原创 论文笔记-Semi-Supervised Learning by Augmented Distribution Alignment
论文信息 论文标题：Semi-Supervised Learning by Augmented Distribution Alignment 论文作者：Qin Wang, Wen Li, Luc Van Gool 研究机构：ETH Zurich; KU Leuven 论文出处：ICCV 2019 引用信息： @inproceedings{DBLP:conf/iccv/WangLG19, author = {Qin Wang and Wen Li a
2020-09-27 20:35:30
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原创 论文笔记-Domain Adaptation for Semantic Segmentation with Maximum Squares Loss
论文信息 论文标题：Domain Adaptation for Semantic Segmentation with Maximum Squares Loss 论文作者：Minghao Chen, Hongyang Xue, Deng Cai 研究机构：浙江大学；阿里巴巴—浙江大学前沿技术联合研究中心 论文出处：ICCV 2019 引用信息： @inproceedings{DBLP:conf/iccv/0001XC19, author = {Minghao Chen and
2020-09-26 23:07:03
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原创 论文笔记-Domain Adaptation for Structured Output via Discriminative Patch Representations
论文信息 论文标题：Domain Adaptation for Structured Output via Discriminative Patch Representations 论文作者： Yi-Hsuan Tsai, Kihyuk Sohn, Samuel Schulter, Manmohan Chandraker 研究机构：NEC 实验室；CMU 论文出处：ICCV 2019 引用信息： @inproceedings{DBLP:conf/iccv/TsaiSSC19, aut
2020-09-25 22:55:41
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原创 论文笔记-Self-Supervised Monocular Depth Hints
论文信息 论文标题：Self-Supervised Monocular Depth Hints 论文作者：Jamie Watson, Michael Firman, Gabriel J. Brostow, Daniyar Turmukhambetov 研究机构：Niantic; UCL 论文出处：ICCV 2019 引用信息： @inproceedings{DBLP:conf/iccv/WatsonFBT19, author = {Jamie Watson and
2020-09-23 18:46:31
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原创 论文笔记-SSF-DAN: Separated Semantic Feature based Domain Adaptation Network for Semantic Segmentation
论文信息 论文标题：SSF-DAN: Separated Semantic Feature based Domain Adaptation Network for Semantic Segmentation 论文作者： Liang Du, Jingang Tan, Hongye Yang, Jianfeng Feng, Xiangyang Xue, Qibao Zheng, Xiaoqing Ye, Xiaolin Zhang 研究机构：中科院上海为系统信息技术所；复旦大学；百度；上海科技大学
2020-09-22 23:12:26
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原创 论文笔记-All about Structure - Adapting Structural Information across Domains for Boosting Semantic Segm
论文信息 论文标题：All about Structure - Adapting Structural Information across Domains for Boosting Semantic Segmentation 论文作者：Wei-Lun Chang, Hui-Po Wang, Wen-Hsiao Peng, Wei-Chen Chiu 研究机构：台湾交通大学 论文出处：CVPR 2019 引用信息： @inproceedings{DBLP:conf/cvpr/ChangW
2020-09-21 22:46:12
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原创 论文笔记-Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
论文信息 论文标题：Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation 论文作者：Seungmin Lee, Dongwan Kim, Namil Kim, Seong-Gyun Jeong 研究机构：首尔国立大学，NAVER LABS， CODE42.ai 论文出处：ICCV 2019 引用信息： @inproceedings{DBLP:conf/iccv/LeeKKJ19
2020-09-20 22:22:53
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原创 论文笔记-Transformation GAN for Unsupervised Image Synthesis and Representation Learning
论文信息 论文标题：Transformation GAN for Unsupervised Image Synthesis and Representation Learning 论文作者：Jiayu Wang, Wengang Zhou, Guo-Jun Qi, Zhongqian Fu, Qi Tian, Houqiang Li 研究机构：中国科学技术大学；Futurewei 论文出处：CVPR 2020 引用信息： @inproceedings{DBLP:conf/cvpr/Wan
2020-09-19 00:32:46
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原创 论文笔记-AdderNet: Do We Really Need Multiplications in Deep Learning?
论文信息 论文标题：AdderNet: Do We Really Need Multiplications in Deep Learning? 论文作者：Hanting Chen, Yunhe Wang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu 研究机构：北京大学、华为诺亚实验室、悉尼大学 论文出处：CVPR 2020 引用信息： @inproceedings{DBLP:conf/cvpr/ChenWXSX0X20, au
2020-09-14 23:22:19
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原创 论文笔记-StereoGAN: Bridging S2R Domain Gap by Joint Optimization Domain Translation and Stereo Matching
论文信息 论文标题：StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching 论文作者：Rui Liu, Chengxi Yang, Wenxiu Sun, Xiaogang Wang, Hongsheng Li 研究机构：商汤，香港中文大学 论文出处：CVPR 2020 引用信息： @inproceedings{DBLP
2020-09-13 20:52:25
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原创 论文笔记-Domain Decluttering Simplifying Images to Mitigate Synthetic-Real Domain Shift...
论文信息 论文标题：Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation 论文作者：Yunhan Zhao, Shu Kong, Daeyun Shin, Charless Fowlkes 研究机构：UC Irvine; Carnegie Mellon University 论文出处：CVPR 2020 引用信息： @inpr
2020-09-12 23:56:58
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原创 论文笔记-SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
论文信息 论文标题：SharinGAN - Combining Synthetic and Real Data for Unsupervised Geometry Estimation 论文作者：Koutilya PNVR, Hao Zhou, David Jacobs 研究机构：University of Maryland, College Park, MD, USA. 论文出处：CVPR 2020 引用信息： @inproceedings{DBLP:conf/cvpr/PNVRZJ2
2020-09-11 23:49:41
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原创 论文笔记-Learning to Adapt Structured Output Space for Semantic Segmentation
论文信息 论文标题：Learning to Adapt Structured Output Space for Semantic Segmentation 论文作者：Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, Manmohan Chandraker 研究机构：NEC Laboratories America; University of California, Merced; Unive
2020-09-10 08:03:06
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原创 论文笔记-Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid（待校正）
论文信息 论文标题：Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid 论文作者：Jun Gao, Zian Wang, Jinchen Xuan, Sanja Fidler 研究机构：University of Toronto, Vector Institute, NVIDIA, Peking University 论文出处：ECCV 2020 引用信息： @articl
2020-09-08 11:26:32
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原创 论文笔记-Unsupervised Domain Adaptation with Dual-Scheme Fusion Network for Medical Image Segmentation
论文信息 论文标题：Unsupervised Domain Adaptation with Dual-Scheme Fusion Network for Medical Image Segmentation 论文作者：Danbing Zou, Qikui Zhu, Pingkun Yan 研究机构：武汉大学，Department of Biomedical Engineering, Troy, NY, USA 论文出处：IJCAI 2020 引用信息： @inproceedings{DB
2020-09-07 11:08:58
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原创 论文笔记-DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
论文信息 论文标题：DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency 论文作者：Yuliang Zou, Zelun Luo, Jia-Bin Huang 研究机构：Virginia Tech; Stanford University 论文出处：ECCV 2018 引用信息： @inproceedings{DBLP:conf/eccv/ZouLH18, author
2020-09-06 17:15:31
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原创 论文笔记-Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement
论文信息 论文标题：Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement 论文作者：Minhyeok Heo, Jaehan Lee, Kyung-Rae Kim, Han-Ul Kim, Chang-Su Kim 研究机构：NAVER LABS; 论文出处：School of Electrical Engineering, Korea University 引用信息：
2020-09-05 10:35:21
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原创 论文笔记-Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
论文信息 论文标题：Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation 论文作者：Zhenyu Zhang, Zhen Cui, Chunyan Xu, Zequn Jie, Xiang Li, Jian Yang 研究机构：Nanjing University of Science and Technology；Tencent AI Lab 论文出处：ECCV 2018 引用信息： @
2020-09-04 21:03:55
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原创 论文笔记-Look Deeper Depth Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss
论文信息 论文标题：Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss 论文作者：Jianbo Jiao, Ying Cao, Yibing Song, Rynson Lau 研究机构：University of Illinois at Urbana-Champaign, Urbana, USA; 腾讯 论文出处：ECCV 2018 引用信息：
2020-09-03 10:56:03
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原创 论文笔记-Structure-Aware Residual Pyramid Network for Monocular Depth Estimation
论文信息 论文标题：Structure-Aware Residual Pyramid Network for Monocular Depth Estimation 论文作者：Xiaotian Chen, Xuejin Chen, and Zheng-Jun Zha 研究机构：中国科学技术大学 论文出处：IJCAI 2019 引用信息： @inproceedings{DBLP:conf/ijcai/ChenCZ19, author = {Xiaotian Chen and
2020-09-02 08:51:34
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原创 论文笔记-Unsupervised Learning of Monocular Depth and Ego-Motion using Conditional PatchGANs
论文信息 论文标题：Unsupervised Learning of Monocular Depth and Ego-Motion using Conditional PatchGANs 论文作者：Madhu Vankadari , Swagat Kumar , Anima Majumder and Kaushik Das 研究机构：TCS Research and Innovation, Bangalore, India 论文出处：IJCAI 2019 引用信息： @inproceed
2020-09-01 09:23:55
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原创 论文笔记-Position-Aware Recalibration Module: Learning From Feature Semantics and Feature Position
论文信息 标题：Position-Aware Recalibration Module: Learning From Feature Semantics and Feature Position 作者：Xu Ma , Song 机构：University of North Texas 出处：IJCAI 2020 代码链接 https://github.com/13952522076/PRM 论文主要贡献 实验结果显示，计算特征图和最具表达力的键-查询操作中的查询点之间的相关性具有实际可..
2020-08-31 22:05:23
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原创 论文笔记-Self-supervised Monocular Depth and VO Learning with Scale-consistent Geometric Constraints
论文信息 标题： Self-supervised Monocular Depth and Visual Odometry Learning with Scale-consistent Geometric Constraints 作者：Mingkang Xiong, Zhenghong Zhang, Weilin Zhong, Jinsheng Ji, Jiyuan Liu, Huilin Xiong 机构：上海交通大学 出处：IJCAI 2020 代码链接 None 论文主要贡献 提出..
2020-08-30 09:25:53
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原创 论文笔记-Context Prior for Scene Segmentation
论文信息 标题： Context Prior for Scene Segmentation 作者：Changqian Yu, Jingbo Wang, Changxin Gao, Gang Yu, Chunhua Shen, Nong Sang 机构：华中科大，The University of Adelaide，香港中文大学，腾讯 出处：CVPR 2020 代码链接 https://git.io/ContextPrior 论文主要贡献 通过亲和力损失 affinity loss 监督..
2020-08-29 23:31:13
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原创 论文笔记-Unsupervised Sketch-to-Photo Synthesis
论文信息 标题： Unsupervised Sketch-to-Photo Synthesis 作者：Runtao Liu, Qian Yu, Stella Yu 机构：北京大学；北航；UCB;ICSI 出处：ECCV 2020 代码链接 None 论文主要贡献 提出第一个两阶段的无监督模型，可以基于手绘草图生成各自不同、和草图结构吻合、风格真实的图像 引入自监督学习目标和注意力模块用于处理草图中的抽象和风格差异问题 模型不仅可以增强基于草图的图像获取，而且能够作为除了图像边缘图之外的..
2020-08-27 20:19:30
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原创 论文笔记-Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network
论文信息 标题： Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network 作者：Xinjing Cheng, Peng Wang, Ruigang Yang 机构：百度研究院 出处：ECCV 2018 代码链接 https://github.com/XinJCheng/CSPN 论文主要贡献 提出 CSPN 模块，比起 SOTA 的传播策略，能够高效、精确地进行深度估计 将..
2020-08-26 18:25:17
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原创 论文笔记-ON THE RELATIONSHIP BETWEEN SELF-ATTENTION AND CONVOLUTIONAL LAYERS
论文信息 标题： ON THE RELATIONSHIP BETWEEN SELF-ATTENTION AND CONVOLUTIONAL LAYERS 作者：Jean-Baptiste Cordonnier, Andreas Loukas & Martin Jaggi 机构：Ecole Polytechnique Federale de Lausanne (EPFL) 出处：ICLR 2020 代码链接 https://github.com/epfml/attention-cnn ..
2020-08-25 21:18:04
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原创 论文笔记-Learning Monocular Depth by Distilling Cross-domain Stereo Networks
论文信息 标题： Learning Monocular Depth by Distilling Cross-domain Stereo Networks 作者：Xiaoyang Guo, Hongsheng Li, Shuai Yi, Jimmy Ren, Xiaogang Wang 机构：商汤科技；香港中文大学 出处：ECCV 2018 代码链接 论文主要贡献 提出新的单目深度估计方法，利用双目立体匹配网络和合成数据的优势，使用立体匹配作为代理任务，合成到真实的跨域问题得到有效解决 新的..
2020-08-24 23:56:10
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原创 论文笔记- Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
论文信息 标题： Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation 作者：Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, Rama Chellappa 机构：University of Maryland, College Park, MD; GE Global Research, Niskayuna, NY; Av..
2020-08-23 23:35:52
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原创 论文笔记-Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement
论文信息 标题： Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement 作者：Yukang Gan, Xiangyu Xu, Wenxiu Sun, Liang Lin 机构：商汤科技、中山大学、清华大学 出处：ECCV 2018 代码链接 None 论文主要贡献 邻域像素的仿射关系建模、改进深度估计的特征提取 垂直方向的特征池化、改进特征聚合对更广泛为的垂直信息加以利用 使..
2020-08-22 22:45:19
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原创 论文笔记-Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
论文信息 标题： Unsupervised Adversarial Depth Estimation using Cycled Generative Networks 作者：Andrea Pilzery, Dan Xuz, Mihai Marian Puscasy, Elisa Ricciy, Nicu Sebey 机构：University of Trento; University of Oxford; Technologies of Vision 出处：3DV 2018 代码链接 htt..
2020-08-20 23:53:16
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原创 论文笔记-Depth Prediction Without the Sensors
论文信息 标题： Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos 作者：Vincent Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova 机构：Harvard University; Google Brain; University of Texas at Austin 出处：AA..
2020-08-20 00:38:03
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原创 论文笔记-Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Tran
论文信息 标题： Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation 作者：Hao Tang, Dan Xu, Nicu Sebe, Yanzhi Wang, Jason J. Corso, Yan Yan 机构：University of Trento, Trento, Italy; Texas State University, San Marc..
2020-08-19 00:17:42
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原创 论文笔记- DOA-GAN: Dual-Order Attentive Generative Adversarial Network for Image Copy-move Forgery Detec
论文信息 标题： DOA-GAN: Dual-Order Attentive Generative Adversarial Network for Image Copy-move Forgery Detection and Localization 作者：Ashraful Islam, Chengjiang Long, Arslan Basharat, Anthony Hoogs 机构：Rensselaer Polytechnic Institute, Troy, NY; Kitware Inc., ..
2020-08-16 17:01:11
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原创 论文笔记-Conditional Convolutions for Instance Segmentation
论文信息 标题： Conditional Convolutions for Instance Segmentation 作者：Zhi Tian, Chunhua Shen, Hao Chen 机构：The University of Adelaide, Australia 出处：ECCV 2020 代码链接 https://git.io/AdelaiDet 论文主要贡献 从新的视角解决实例分割问题，提出 CondInst 实例分割网络，改进了当前的实例分割方法的性能，同时达到更快的推理..
2020-08-16 16:59:50
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原创 论文笔记-Learning Semantic Segmentation from Synthetic Data Geometrically Guided Input-Output Adaptation
论文信息 标题： Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach 作者：Yuhua Chen, Wen Li, Xiaoran Chen, Luc Van Gool 机构：Computer Vision Laboratory, ETH Zurich; VISICS, ESAT/PSI, KU Leuven 代码链接 http:..
2020-08-14 15:41:15
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原创 论文笔记-Learning Stereo from Single Images
论文信息 标题： Learning Stereo from Single Images 作者：Jamie Watson, Oisin Mac Aodha, Daniyar Turmukhambetov, Gabriel J. Brostow, Michael Firman 机构：Niantic, University of Edinburgh, UCL 代码链接 https://github.com/nianticlabs/stereo-from-mono/ 论文主要贡献 提出自动的生..
2020-08-13 15:20:28
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