何乾梁 - 幻觉 - Single
何乾梁 - 笑柄 (粤语版) - Single
克里夫 - 路 - SIngle
Yang Dail - Absence - Single
念慈ParE - 关机又关机 - Single
JUNNY - HIDE & SICK - Single
Max Jenmana - วันนี้ฉันอยากไปทะเล (Into The Sea) - Single
Max Jenmana - อย่าฝันเลย - Single
Max Jenmana - Joyride - Single
何乾梁 - 笑柄 (粤语版) - Single
克里夫 - 路 - SIngle
Yang Dail - Absence - Single
念慈ParE - 关机又关机 - Single
JUNNY - HIDE & SICK - Single
Max Jenmana - วันนี้ฉันอยากไปทะเล (Into The Sea) - Single
Max Jenmana - อย่าฝันเลย - Single
Max Jenmana - Joyride - Single
这是一枚分外特别又极为迷人的DATEJUST日志型腕表,型号1600,1968年瑞士生产,不锈钢材质蚝式防水表壳直径36mm,不同于司空见惯的三角坑纹外圈,表圈采用光滑的18K黄金材质,低调而圆润,原本黑色的表盘,被神秘的时间力量幻化出层层星星点点,金质的劳力士皇冠标志,立体刻度造型精致,中央拱起尾端斜坡,高抛光镜面处理,令在不同光线角度之下,金质刻度都熠熠生辉。必须特别介绍的是表盘上的金色字体与表盘边缘的刻度并不是以常见的方式印在黑色底色之上,而是采用黑色底色镂空出字体与刻度的方式,所以仔细观察可以发现,字体与刻度会比黑色区域更低,透露出表盘基底的金色镀层。使用这种工艺的表盘被称作Gilt Dail,仅在少许古董表上才能觅见其踪影。
内部装载著名的26钻1570机芯,这款伟大的机芯被誉为一代自动上链机芯之王,强悍可靠,性能卓越。摆频由上一代1560机芯的18,800bph提高到19,800bph、精准度进一步提升。宽大厚实的金质把头,侧面有深深的三角坑纹,正面浮现着劳力士皇冠标志和一条短线,一条短线表示:把头具备Twinlock双重锁扣防水系统,把头与把管均内置有防水胶圈,保证防水可以深达100米,亦代表采用不锈钢或黄金材质。不锈钢材质蚝式表壳的表耳采用穿孔的设计,直截了当,更换表带时非常方便。
搭配劳力士专为DATEJUST腕表精心设计的“纪念型五珠表带”,漂亮的五排圆拱形珠链错落相连,金与钢交相呼应,表链外侧较宽链节采用不锈钢材质拉丝打磨修饰,中央三排18K黄金链节则高抛光处理,令整条纪念型五珠表带有着丰富的层次感,充分彰显出典雅的气质与精致的格调。
希望你也会喜欢这些古董表
内部装载著名的26钻1570机芯,这款伟大的机芯被誉为一代自动上链机芯之王,强悍可靠,性能卓越。摆频由上一代1560机芯的18,800bph提高到19,800bph、精准度进一步提升。宽大厚实的金质把头,侧面有深深的三角坑纹,正面浮现着劳力士皇冠标志和一条短线,一条短线表示:把头具备Twinlock双重锁扣防水系统,把头与把管均内置有防水胶圈,保证防水可以深达100米,亦代表采用不锈钢或黄金材质。不锈钢材质蚝式表壳的表耳采用穿孔的设计,直截了当,更换表带时非常方便。
搭配劳力士专为DATEJUST腕表精心设计的“纪念型五珠表带”,漂亮的五排圆拱形珠链错落相连,金与钢交相呼应,表链外侧较宽链节采用不锈钢材质拉丝打磨修饰,中央三排18K黄金链节则高抛光处理,令整条纪念型五珠表带有着丰富的层次感,充分彰显出典雅的气质与精致的格调。
希望你也会喜欢这些古董表
几篇论文实现代码:
《Explore Image Deblurring via Encoded Blur Kernel Space》(CVPR 2021) GitHub:https:// github.com/VinAIResearch/blur-kernel-space-exploring [FIG7]
《VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation》(ICCV 2021) GitHub:https:// github.com/hzykent/VMNet [fig1]
《Greedy Gradient Ensemble for Robust Visual Question Answering》(ICCV 2021) GitHub:https:// github.com/GeraldHan/GGE
《Generalized Source-free Domain Adaptation》(ICCV 2021) GitHub:https:// github.com/Albert0147/G-SFDA
《Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP》(2021) GitHub:https:// github.com/timoschick/self-debiasing
《MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks》(NeurIPS 2021) GitHub:https:// github.com/alexrame/mixmo-pytorch [fig6]
《Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture》(2021) GitHub:https:// github.com/CDInstitute/Building-Dataset-Generator [fig5]
《PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features》(2021) GitHub:https:// github.com/qq456cvb/SPRIN [fig2]
《Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture》(2021) GitHub:https:// github.com/CDInstitute/Building-Dataset-Generator [fig4]
《PHASER: A Robust and Correspondence-Free Global Pointcloud Registration》(2021) GitHub:https:// github.com/ethz-asl/phaser [fig3]
《Text-Based Ideal Points》(ACL 2020) GitHub:https:// github.com/keyonvafa/tbip
《Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation》(ECCV 2020) GitHub:https:// github.com/TKKim93/APE
《Domain Adaptive Imitation Learning》(2020) GitHub:https:// github.com/ermongroup/dail
《Training Generative Adversarial Networks by Solving Ordinary Differential Equations》(2020) GitHub:https:// github.com/titu1994/pytorch_odegan
《SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning》(2020) GitHub:https:// github.com/cjrd/selfaugment
《Explore Image Deblurring via Encoded Blur Kernel Space》(CVPR 2021) GitHub:https:// github.com/VinAIResearch/blur-kernel-space-exploring [FIG7]
《VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation》(ICCV 2021) GitHub:https:// github.com/hzykent/VMNet [fig1]
《Greedy Gradient Ensemble for Robust Visual Question Answering》(ICCV 2021) GitHub:https:// github.com/GeraldHan/GGE
《Generalized Source-free Domain Adaptation》(ICCV 2021) GitHub:https:// github.com/Albert0147/G-SFDA
《Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP》(2021) GitHub:https:// github.com/timoschick/self-debiasing
《MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks》(NeurIPS 2021) GitHub:https:// github.com/alexrame/mixmo-pytorch [fig6]
《Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture》(2021) GitHub:https:// github.com/CDInstitute/Building-Dataset-Generator [fig5]
《PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features》(2021) GitHub:https:// github.com/qq456cvb/SPRIN [fig2]
《Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture》(2021) GitHub:https:// github.com/CDInstitute/Building-Dataset-Generator [fig4]
《PHASER: A Robust and Correspondence-Free Global Pointcloud Registration》(2021) GitHub:https:// github.com/ethz-asl/phaser [fig3]
《Text-Based Ideal Points》(ACL 2020) GitHub:https:// github.com/keyonvafa/tbip
《Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation》(ECCV 2020) GitHub:https:// github.com/TKKim93/APE
《Domain Adaptive Imitation Learning》(2020) GitHub:https:// github.com/ermongroup/dail
《Training Generative Adversarial Networks by Solving Ordinary Differential Equations》(2020) GitHub:https:// github.com/titu1994/pytorch_odegan
《SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning》(2020) GitHub:https:// github.com/cjrd/selfaugment
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