一年一版本,Sublime Text 3.2 正式发布

3 月 14 日 开源中国

今天 Sublime HQ 正式发布了 Sublime Text 3.2 版本,距离上个重要版本(Sublime Text 3.1)更新已过去了将近一年的时间。


Sublime Text 3.2

据官方介绍,该版本值得关注的新特性包括:

  • 完美集成 Git

  • 直观显示文件的增量变化(incremental diffing)

  • 针对主题功能的变更和改进

  • 对代码块(block caret)的支持

  • 语法高亮新增对 Clojure, D, Go, Lua 语言的支持

3.2 更新日志

除此之外还有一系列的其他功能增强、稳定性改进和性能提升。


△该图显示了侧栏的 Git 状态标记,以及在编辑器中添加、修改和删除的行

详情请查看 https://www.sublimetext.com/blog/articles/sublime-text-3-point-2
Sublime Text 3.2 下载地址 https://www.sublimetext.com/3


开源中国征稿开始啦!


开源中国 www.oschina.net 是目前备受关注、具有强大影响力的开源技术社区,拥有超过 200 万的开源技术精英。我们传播开源的理念,推广开源项目,为 IT 开发者提供一个发现、使用、并交流开源技术的平台。


现在我们开始对外征稿啦!如果你有优秀的技术文章想要分享,热点的行业资讯需要报道等等,欢迎联系开源中国进行投稿。投稿详情及联系方式请参见:我要投稿


推荐阅读

F5 收购 NGINX

Vue.js 作者尤雨溪:开源给了我无价的自由

绿得发慌?最强 IDE VS2019 修复“绿帽子”问题

CSS 宣布支持三角函数,下一步是什么?

又是求职季,这份面试宝典送给你

「好看」一下,分享给更多人↓↓

登录查看更多
点赞 0

We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Neural Network (CNN) for Hand-held Object Pose and Shape estimation called HOPS-Net and utilize prior work to estimate the hand pose and configuration. We leverage the insight that information about the hand facilitates object pose and shape estimation by incorporating the hand into both training and inference of the object pose and shape as well as the refinement of the estimated pose. The network is trained on a large synthetic dataset of objects in interaction with a human hand. To bridge the gap between real and synthetic images, we employ an image-to-image translation model (Augmented CycleGAN) that generates realistically textured objects given a synthetic rendering. This provides a scalable way of generating annotated data for training HOPS-Net. Our quantitative experiments show that even noisy hand parameters significantly help object pose and shape estimation. The qualitative experiments show results of pose and shape estimation of objects held by a hand "in the wild".

点赞 0
阅读2+

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.

点赞 0
阅读1+

We propose a person detector on omnidirectional images, an accurate method to generate minimal enclosing rectangles of persons. The basic idea is to adapt the qualitative detection performance of a convolutional neural network based method, namely YOLOv2 to fish-eye images. The design of our approach picks up the idea of a state-of-the-art object detector and highly overlapping areas of images with their regions of interests. This overlap reduces the number of false negatives. Based on the raw bounding boxes of the detector we fine-tuned overlapping bounding boxes by three approaches: the non-maximum suppression, the soft non-maximum suppression and the soft non-maximum suppression with Gaussian smoothing. The evaluation was done on the PIROPO database and an own annotated Flat dataset, supplemented with bounding boxes on omnidirectional images. We achieve an average precision of 64.4 % with YOLOv2 for the class person on PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum suppression with Gaussian smoothing.

点赞 0
阅读2+
Top