Striking-Warning9533 | 1 points | Oct 05 2021 01:02:41

看到昨天那个抱怨的垃圾英文邮件，发现其实自己的英文也挺差的。希望各位浪人幫我看看，这是我随手写的还没有复查

A Brief Comparison Between State-Of-The-Art Computer Vision Models

Computer vision is an important field in the machine learning field, and it is also common in our daily lives. In this article, we will go through some common state-of-the-art models in computer vision: YOLO (You Only Look Once), Mask R-CNN (Mask Regional Convolutional Neural Network), and U-Net. Although they are all really frequently used in computer vision projects, they actually have different usage cases and performances.

The first thing we are going to talk about is the usage of the models. They are all capable of localizing object(s) in an image, comparing to image classification models, which can only give the names of objects in the image. For example, if we have a photo of a study room and we feed this image into an image classification model and also models we mentioned before. The image classification model might give the output of all the names like “table, lamp, window”. On the other hand, the three models we mentioned before could do more than that: they can give the location information of the objects.

Despite this, there are still differences between them. YOLO is an image detection model, which will create a bounding box of each object in the image. This could be useful to measure how many people are on the street or detect and trace human hands for gesture control. On the other hand, the U-Net and Mask R-CNN are both image segmentation models. This kind of model will assign each pixel a label, giving a more precise result. It has been used in autopilot and many medical lesion localization; in fact, this is the reason that U-Net is proposed. Nonetheless, U-Net is a semantic segmentation model while Mask R-CNN is an instance segmentation model. A semantic segmentation model will assign all the objects in the same catalog with the same label, whereas an instance segmentation will give every single object a label. Imagine we have a photo of a school. The semantic segmentation model will label the whole crowd the same label “human”, and an instance segmentation model will, or at least will try to, assign each person a label, like “person_1”, “person_2”, etc.

[-] watchingsunset | 18 points | Oct 05 2021 01:05:10

比Hu同学写得好

[-] MundaneAssociation71 | 7 points | Oct 05 2021 01:32:25

比他不知道高到哪里起来。就他那个英语水平，我怀疑他日常生活都不能自理

[-] ban-ni-ma | 3 points | Oct 05 2021 01:06:14

可以入典了

[-] OddBird_S | 5 points | Oct 05 2021 02:38:53

hu同学小作文可以编入聊斋志异了

[-] Striking-Warning9533 | 7 points | Oct 05 2021 01:37:20

妈的，刚才在Grammarly说我抄袭。。。。这速度也太快了吧。反正就是一个英语作业，发在reddit了也没事

https://imgur.com/a/Zi6xSM9

[-] CheesecakeDestoryer | 7 points | Oct 05 2021 02:12:02

Soviet united 认为你的文章不够ours

[-] Kuonji_Alice | 5 points | Oct 05 2021 01:36:34

你可以直接把内容复制到grammarly上，我觉得这网站查语法错误挺好用的。

[-] Striking-Warning9533 | 9 points | Oct 05 2021 01:38:38

我有，而且学校送了所有国际学生专业版。然后刚才被grammarly说剽窃这个Reddit的内容。。。

[-] wuwuwucg | 7 points | Oct 05 2021 02:44:19

自己剽窃自己，挺牛逼的

[-] Striking-Warning9533 | 9 points | Oct 05 2021 02:44:47

牛逼的是会检查reddit，而且这么快

[-] wuwuwucg | 7 points | Oct 05 2021 02:46:14

第一次知道grammarly还会检查source

[-] Striking-Warning9533 | 2 points | Oct 05 2021 02:46:42

要专业版

[-] wuwuwucg | 4 points | Oct 05 2021 02:48:38

我這個窮小子就白嫖免費版好了

[-] Striking-Warning9533 | 2 points | Oct 05 2021 02:49:13

强烈不建议买，没啥么用。我用完这一年就不用了

[-] wuwuwucg | 2 points | Oct 05 2021 02:50:20

好的哈哈，等我以後寫paper發NEJM的時候還請各位大哥瞧瞧

[-] Proof_Instruction455 | 1 points | Oct 05 2021 02:34:26

哪儿的学校这么嗯

[-] Striking-Warning9533 | 2 points | Oct 05 2021 02:38:03

刚才看了一下，学校送的是rosetta stone， grammarly是我自己买的

[-] MundaneAssociation71 | 4 points | Oct 05 2021 01:29:13

他那个真的是小学水平

[-] OddBird_S | 3 points | Oct 05 2021 02:38:19

上面说你语言太支，我觉得就是句子结构不够干净利落。一是太多一个topic分多个句子，this这种可以用伴随状态合成一句，不打断思维。二是用重复单词和陈词滥调充字数，尤其是最后一段简直稀烂。我建议你写完first draft后小声读出来一遍，把不必要的部分去掉（前提是你没有字数的压力）。此外，适当用更加丰富的词汇能提高你的表达能力和准确性。我给你改最后一段：

One should note that there are other differences apart from the aforementioned ones, and advantages of either model solely depend on the context. Thus, selection of a model for a project should be made with deliberation, taking relevant researches and contingencies into account.

[-] SheJeanPin | 1 points | Oct 05 2021 01:51:43

第一句就支风英语，语法啥的问题

[-] Striking-Warning9533 | 2 points | Oct 05 2021 01:52:44

第一句太罗嗦了在当时脑抽才写成这样。已经改了Computer vision is important in the machine learning field, and it is also common in our daily lives.

[-] OddBird_S | 1 points | Oct 05 2021 02:44:08

一个主语不必分两句

[-] welcome1233 | 1 points | Oct 05 2021 02:20:06

只改第一句 Computer vision is a field in machine learning that extract and analyze visual information using computer. It has a variety of applications in our daily life namely Autonomous Driving, Medical Imaging and Public safety.

[-] OddBird_S | 1 points | Oct 05 2021 02:40:16

extracts analyzes , namely

[-] qwedsa789654 | 1 points | Oct 05 2021 04:04:26

好哎

Computer vision is an important field in the machine learning field, 別又and又also and it is also common in our daily lives . Although they are all really frequently *used in computer vision projects, they actually have different usage cases and performances..

Besides these three main differences, there are also other differences. And in some cases, one of these models just works better. Thus, it is important to research and decide which model should be used in a project

沒太古怪吧，下方倒是提個case呀

[-] [deleted] | 1 points | Oct 05 2021 04:04:39

[removed]

[-] jingleonline | 1 points | Oct 05 2021 05:23:28

大概国内高三-大一水平？用词尤其是连接词有股浓郁的高考作文感。我觉得提高学术写作水平最快的办法是海量读不同领域paper。顺便提一句内容，学术写作里专有名词一定要严谨，比如vision方向提bounding box的那个叫object detection不叫image detection。话说回来这三个网络都不是一个作用，放一起比较本身就很奇怪，尤其考虑到你的标题就很vague，不提task/metrics/dataset何来SOTA一说

[-] Striking-Warning9533 | 1 points | Oct 05 2021 05:30:30

同是好。 这不是专业课的paper，而是英语课的。

[-] Striking-Warning9533 | 1 points | Oct 05 2021 05:33:11

这三个模型不是都适用于定位吗？我在做的一个项目是定位白板笔把白板上的2字迹变成数位版本。用这三种都可以。当然最后的用的是根据yolo修改的

[-] 11111010 | 1 points | Oct 05 2021 08:06:39

太长不看

[-] lee_jianguo | 1 points | Oct 05 2021 10:03:05

主要问题是我觉得比较啰嗦。比如第二段

The first thing we are going to talk about is the usage of the models. They are all capable of localizing object(s) in an image, comparing to image classification models, which can only give the names of objects in the image. For example, if we have a photo of a study room and we feed this image into an image classification model and also models we mentioned before. The image classification model might give the output of all the names like “table, lamp, window”. On the other hand, the three models we mentioned before could do more than that: they can give the location information of the objects.

我会写为：

The usage of the three models is similar. When fed with an image, say, of a study room, these models can locate objects, such as tables or lamps, in it. This is more powerful than traditional image classification models, which can only name the objects but give no location information.

其实这个具体的example（study room）也许并没有必要，不过即使加进去也尽量concise一些。当然这段想要更concise一些写成一句话都是可以的。