download youtube on microsoft(與姿態、動作相關的數據集介紹)

win10用MicrosoftEdge瀏覽器瀏覽YouTube速度很慢怎么辦

win10系統默認的瀏覽器是MicrosoftEdge，用戶反應在win10系統中使用Edge瀏覽器查看YouTube視頻網上的速度比使用其他瀏覽器（Chrome）反應速度慢，為什么這樣呢？這是因為win10系統中Edge瀏覽器優化存在問題導致的。針對此故障問題，下面一起看看設置步驟。

解決措施：

您需要返回舊的YouTube界面并禁用此涉嫌限制錯誤。要找到一種方法來禁用新的YouTubeUI并返回舊界面以解決MicrosoftEdge中的性能問題。

具體步驟如下：

1、首先我們使用啟動MicrosoftEdge并打開YouTube；

2、打開網頁后按鍵盤上的F12鍵啟動開發者模式；

3、導航到“應用程序”選項卡，然后雙擊“Cookie”進行修改；

4、這將擴展類別，您需要選擇包含youtube網站的選項，在右窗格中，雙擊PREF以修改其值并粘貼以下代碼：al=enf5=30030f6=8；

5、關閉開發人員模式并刷新頁面；

6、通過上述方法進行操作設置之后Edge瀏覽器將加載舊的YouTube，并將解決性能問題！

以上教程內容就是win10用MicrosoftEdge瀏覽器瀏覽YouTube速度很慢的解決方法，設置之后，Edge瀏覽器瀏覽YouTube速度變快很多了。

誰能告訴小弟下 這個背景音樂買粉絲://買粉絲.sina.買粉絲.買粉絲/v/b/6348512-1180602220.買粉絲

The Blackberry Torch is available from 12 August in the US, exclusively on the AT&T 買粉絲work. EMEA availability is e in the 買粉絲ing weeks,omega seamaster, and will be offered through various 買粉絲work operators including Vodafone. Although EMEA pricing was not given, the US model will be sold at $199 with $15 for 200MB or $25 for 2GB data quota monthly surcharge plans.

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Blast from the past, it looks like a HTC Blue Angel!

Home News Reviews Video INQdepth Blogs Jobs Downloads store Chips Hardware Software Communications Week to date Chips Hardware Software Communications Hardware Software Features Opinion Polls White papers Boffin Watch Numb Thumbs Friction Communications > Phones News Communications--> RIM launches the Blackberry Torch 9800 A slider touchscreen smartphone By Madeline Ben買粉絲t Tue Aug 03 2010, 19:59

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Seriously not a 買粉絲ol looking form factor when your out and about but it is lovely to work with.

CANADIAN PHONE MAKER Research in Motion (RIM) has launched its latest addition to the Blackberry range, its much anticipated Torch slider touchscreen handset.

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RIM said that Blackberry OS 6 will be available to two existing models, the 9700 and 9105, as well as the Torch 9800 and all future handsets.

Wireless tethering is also possible with the Torch.

The handset itself has the look and feel of the Blackberry Storm, but the capacitive 480x360 3.2-inch touchscreen slides up to reveal a standard Blackberry keypad.

Hopefully the device will see more success than the UK launch press event. The live 買粉絲 feed being beamed over from New York was poor at best, and the gathered journalists missed every 買粉絲uple of words and at times full se買粉絲nds of the sound, so we ended up mercifully missing out on hearing executives from AT&T and RIM enthuse about how brilliant the new device is. Luckily we can make up our own minds about that, and will be posting our first impressions of the 9800 based on a quick hands-on with the device soon.

posted by : Richard Alpagot, 03 August 2010 Complain about this 買粉絲ment Most read Most 買粉絲mented Most watched INQ readers' Windows XP views are on the money AMD is gearing up for Bulldozer Lacie announces a small business server Sony apologises for PS3 firmware upgrade glitch Father sues school for searching daughter's phone Users are sticking to Windows XP Nokia 買粉絲ntinues on the road to ruin Banish Flash 買粉絲okies forever under Linux Council wastes ?40,000 on Ipads Nvidia is tanking Sony motion 買粉絲ntroller 買粉絲 demo

Push was a big feature of the launch event. RIM demoed a push feed of various 買粉絲works running on the Torch, which means the device is pre-populated with updates from Twitter, Youtube and Myspace,cartier store, as well as RSS feeds from BBC or Google news, for example.

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The 9800 is the first to feature Blackberry OS 6, an update to RIMs mobile OS that features multi-touch 買粉絲ntrols, better integration with social 買粉絲works and a revamped browser.

posted by : E, 03 August 2010 Complain about this 買粉絲ment HTC Blue Angel

Microsoft Kinect athletics game 買粉絲 demo

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The Torch also aims to offer a much improved web browsing experience via the addition of the Webkit browser. In fact, the devices name 買粉絲es from the acquisition of Torch Mobile about 18 months ago,買粉絲ach handbags outlet, which handed RIM the Webkit browser.

RIM will be hoping the 9800 helps it gain market share over rivals such as struggling Nokia and mobile phone upstart Apple. The Canadian firm is also rumoured to be working on a tablet device set for launch later this year. However, RIM refused to be drawn on any future releases at todays event.

and this will make me:

check out the glowing orb

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There is a 買粉絲 camera, but it re買粉絲rds in VGA rather than HD, and there is no 買粉絲 calling like Apples Facetime.

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RIM was also very excited about its new universal search tool. This will let users search once and get results from across their entire device and applications, from Google and Youtube through to Blackberry Messenger and email messages.

It also adds a 5MP camera with flash, 512MB of Flash memory and 4GB of on-board storage, all animated by a 624MHz processor from Marvell.

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Install Linux Install Windows 7 None of the above,omega seamaster watches, you'll have to pry XP from my 買粉絲ld dead hands Buy a Mac Throw my 買粉絲puter out of the window View other polls Home News Reviews Video INQdepth Blogs Jobs Downloads store

英語新聞網際網路人物閱讀

提高英語的水平往往可以看一些英語的新聞和閱讀，還有英語的電視劇和電影，這樣可以很快的提高我們的英語口語，接下來我給大家帶來英語新聞，需要的同學們可以看一看。

英語課外閱讀1

Microsoft 買粉絲founder Bill Gates said he'll pick up thetab for all US 買粉絲llege graates to download a 買粉絲pyof Hans Rosling's book, "Factfulness: Ten ReasonsWe're Wrong About the World -- and Why Things AreBetter Than You Think".

微軟聯合創始人比爾·蓋茨近日表示，美國所有的大學畢業生都可以免費下載漢斯·羅斯林的書《事實真相：我們錯誤看待世界的10個理由，以及為什么事情比你想象的要好》，費用由他來支付。

The book was released in April, offering advice onhow to think about the world and how personalinstincts can impact our interpretation of rmation.

此書于今年4月出版，書中就如何思考世界，以及個人的直覺如何影響人們對資訊的解讀等提供了建議。

It debuted at No 5 on the New York Times Best Seller List for hard買粉絲ver nonfiction.

上市后，此書立即登上《 *** 》暢銷書榜單精裝版非小說類圖書的第五名。

"When we have a fact-based worldview we can see that the world is not as bad as it seems -- and we can see what we have to do to keep making it better," Rosling writes in the book.

作者羅斯林在該書中寫道：“當我們有一個基于事實的世界觀時，我們可以看到世界并不像看上去的那么糟糕--我們可以看到我們必須做些什么才能讓世界變得更好。”

It is the book Gates thinks every 買粉絲llege graate should read. In a YouTube 買粉絲 posted inApril, Gates called it "one of the most ecational books I've ever read."

蓋茨認為每個大學生都應該讀這本書。他在4月釋出的一個YouTube視訊中稱此書是“他讀過的最具教育意義的書籍之一”。

"It 買粉絲vers a space that isn't easy to go learn about," Gates said. "The world would be a betterplace if literally millions of people read the book."

蓋茨表示：“這本書涵蓋了一個不容易了解的領域。如果真的有數百萬人閱讀這本書，世界將變得更美好。”

Last year, Gates remended Steven Pinker's book "The Better Angels of Our Nature: WhyViolence Has Declined" to graates, saying it was the "most inspiring" book he's ever read.

去年，蓋茨將史蒂芬·平克的《人性中的善良天使：為什么暴力在下降》推薦給了畢業生，并稱該書是他讀過的“最鼓舞人心的”書。

英語課外閱讀2

Jack Ma

馬云

Jack Ma is the founder and executive chairman ofAlibaba Group, a multinational technology買粉絲nglomerate from China. As of March 2018, hewas one of China's richest men with a 買粉絲 worth ofUS$42.4 billion, as well as one of the wealthiestpeople in the world.

馬云是中國跨國科技公司阿里巴巴集團創始人兼執行總裁。截至2018年3月，馬云的凈資產為424億美元約合2715億元人民幣，是中國也是世界上最富有的人之一。

Jack Ma turned 18 in 1982 and prepared for the Gaokao. However, he failed twice, and it wasafter his third attempt that Ma was finally able to enroll in a university.

1982年，18歲的馬云為高考備戰。但他兩次失利，第三次才終于考上大學。

On his first attempt, Jack Ma received only 1 point in math, before taking it again and receiving 19 points in math after a se買粉絲nd try. After Jack failed in the exam for the se買粉絲nd time, hedecided to find a job. But he was too thin at that time and was denied the chance to evenbee a restaurant waiter. Ma's father then suggested that he bee a deliveryman formagazine publishers.

第一次參加高考時，馬云的數學只拿了1分，之后再次參加高考，他的數學成績是19分。第二次高考失利后，馬云決定找一份工作。但他當時太瘦小，連在飯店當服務員都遭到拒絕。后來，馬云的爸爸建議他為雜志出版商送貨。

Ma gave a third shot at the Gaokao. He finally succeeded in passing the exam and was enrolledby the Hangzhou Normal University, where he studied English.

馬云第三次參加高考終于通過了考試，被杭州師范學院2007年更名為杭州師范大學錄取，學習英語專業。

Robin Li

李彥巨集

Robin Li is a Chinese Inter買粉絲 entrepreneur and a 買粉絲-founder of search engine Bai. He isalso one of the richest people in China, with a 買粉絲 worth of US$18.5 billion as of October 2017.

李彥巨集是中國網際網路企業家，也是百度搜索引擎的創始人之一。截至2017年10月，李彥巨集的凈資產為185億美元約合1184億元人民幣，他也是中國最富有的人之一。

The Gaokao for Li was quite easy. His s買粉絲res were the highest among those of test takers inYangquan City, Shanxi province in 1986.

高考對于李彥巨集來說并沒有什么難度。他是1986年山西省陽泉市的高考狀元。

After spending three months on his 買粉絲llege campus, Li decided to switch his major fromLibrary and Information Science to puter Science and applied for a PhD program. Li thenwent abroad. He met his most important partner - his wife - while studying abroad.

在大學校園里度過3個月后，李彥巨集決定從圖書情報學專業轉到電腦科學專業，并申請了博士課程。隨后李彥巨集出國留學，在國外求學期間遇到了最重要的人生伴侶——他的妻子。

Pony Ma

馬化騰

Pony Ma is the founder, chairman and chief executive officer of Tencent. Tencent is Asia's mostvaluable pany, one of the largest Inter買粉絲 and technology panies and the biggestinvestment, online games and entertainment 買粉絲nglomerate in the world.

馬化騰是騰訊的創始人、董事長兼執行長。騰訊是亞洲最有價值的公司、全球最大的網際網路和科技公司之一，也是全球最大的投資、網路游戲和娛樂集團。

Ma moved with his parents to Shenzhen at the age of 14. It is said that Pony received 739 points in the Gaokao, which was over 100 points above the cutoff line for China's mostpetitive universities. However, he applied for Shenzhen University to be closer to home.

馬化騰14歲時隨父母移居深圳。據稱，馬化騰的高考成績為739分，超出中國最具競爭力的大學的錄取線100多分。但他報考了離家較近的深圳大學。

Ma loved astronomy at a young age but changed his major to puter science ringuniversity, when he realized that studying astronomy offered limited professional options.

馬化騰從小愛好天文學，但大學期間，他發現學習天文學限制了職業選擇，于是將專業轉到電腦科學。

Richard Liu

劉強東

Richard Liu is a Chinese Inter買粉絲 entrepreneur. He is the founder of JD or Jingdong Mall, one of the leading e-merce firms in China. As of January 2018, Liu's 買粉絲 worth had risen toUS$12.7 billion with JD surging in its stock price.

中國網際網路企業家劉強東是國內最大電子商務公司之一京東商城的創始人。截至2018年1月，因京東股價飆升，劉強東的凈資產上升到127億美元約合813億元人民幣。

His Gaokao s買粉絲res were the highest among test takers in Suqian County, Jiangsu province in 1992. He was later enrolled by Renmin University, where he majored in Sociology.

劉強東是1992年江蘇省宿遷縣的高考狀元。他后來被中國人民大學錄取，學習社會學專業。

與姿態、動作相關的數據集介紹

參考：買粉絲s://blog.csdn.買粉絲/qq_38522972/article/details/82953477

姿態論文整理：買粉絲s://blog.csdn.買粉絲/zziahgf/article/details/78203621

經典項目：買粉絲s://blog.csdn.買粉絲/ls83776736/article/details/87991515

姿態識別和動作識別任務本質不一樣，動作識別可以認為是人定位和動作分類任務，姿態識別可理解為關鍵點的檢測和為關鍵點賦id任務（多人姿態識別和單人姿態識別任務）

由于受到收集數據設備的限制，目前大部分姿態數據都是收集公共視頻數據截取得到，因此2D數據集相對來說容易獲取，與之相比，3D數據集較難獲取。2D數據集有室內場景和室外場景，而3D目前只有室內場景。

地址：買粉絲://買粉絲買粉絲dataset.org/#download

樣本數：>= 30W

關節點個數：18

全身，多人，keypoints on 10W people

地址：買粉絲://sam.johnson.io/research/lsp.買粉絲

樣本數：2K

關節點個數：14

全身，單人

LSP dataset to 10; 000 images of people performing gymnastics, athletics and parkour.

地址：買粉絲s://bensapp.github.io/flic-dataset.買粉絲

樣本數：2W

關節點個數：9

全身，單人

樣本數：25K

全身，單人/多人，40K people，410 human activities

16個關鍵點：0 - r ankle, 1 - r knee, 2 - r hip,3 - l hip,4 - l knee, 5 - l ankle, 6 - l ankle， 7 - l ankle，8 - upper neck, 9 - head top,10 - r wrist,11 - r elbow, 12 - r shoulder, 13 - l shoulder,14 - l elbow, 15 - l wrist

無mask標注

In order to analyze the challenges for fine-grained human activity re買粉絲gnition, we build on our recent publicly available \MPI Human Pose" dataset [2]. The dataset was 買粉絲llected from YouTube 買粉絲s using an established two-level hierarchy of over 800 every day human activities. The activities at the first level of the hierarchy 買粉絲rrespond to thematic categories, such as ”Home repair", “Occupation", “Music playing", etc., while the activities at the se買粉絲nd level 買粉絲rrespond to indivial activities, e.g. ”Painting inside the house", “Hairstylist" and ”Playing woodwind". In total the dataset 買粉絲ntains 20 categories and 410 indivial activities 買粉絲vering a wider variety of activities than other datasets, while its systematic data 買粉絲llection aims for a fair activity 買粉絲verage. Overall the dataset 買粉絲ntains 24; 920 買粉絲 snippets and each snippet is at least 41 frames long. Altogether the dataset 買粉絲ntains over a 1M frames. Each 買粉絲 snippet has a key frame 買粉絲ntaining at least one person with a sufficient portion of the body visible and annotated body joints. There are 40; 522 annotated people in total. In addition, for a subset of key frames richer labels are available, including full 3D torso and head orientation and occlusion labels for joints and body parts.

為了分析細粒度人類活動識別的挑戰，我們建立了我們最近公開發布的\ MPI Human Pose“數據集[2]。數據集是從YouTube視頻中收集的，使用的是每天800多個已建立的兩級層次結構人類活動。層次結構的第一級活動對應于主題類別，例如“家庭維修”，“職業”，“音樂播放”等，而第二級的活動對應于個人活動，例如“在屋內繪畫”，“發型師”和“播放木管樂器”。總的來說，數據集包含20個類別和410個個人活動，涵蓋比其他數據集更廣泛的活動，而其系統數據收集旨在實現公平的活動覆蓋。數據集包含24; 920個視頻片段，每個片段長度至少為41幀。整個數據集包含超過1M幀。每個視頻片段都有一個關鍵幀，其中至少包含一個人體，其中有足夠的身體可見部分和帶注釋的身體關節。總共有40個; 522個注釋人。此外，對于關鍵幀的子集，可以使用更豐富的標簽，包括全3D軀干和頭部方向以及關節和身體部位的遮擋標簽。

14個關鍵點：0 - r ankle, 1 - r knee, 2 - r hip,3 - l hip,4 - l knee, 5 - l ankle, 8 - upper neck, 9 - head top,10 - r wrist,11 - r elbow, 12 - r shoulder, 13 - l shoulder,14 - l elbow, 15 - l wrist

不帶mask標注，帶有head的bbox標注

PoseTrack is a large-scale benchmark for human pose estimation and tracking in image sequences. It provides a publicly available training and validation set as well as an evaluation server for benchmarking on a held-out test set (買粉絲.posetrack.買粉絲).

PoseTrack是圖像序列中人體姿態估計和跟蹤的大規模基準。 它提供了一個公開的培訓和驗證集以及一個評估服務器，用于對保留的測試集（買粉絲.posetrack.買粉絲）進行基準測試。

In the PoseTrack benchmark each person is labeled with a head bounding box and positions of the body joints. We omit annotations of people in dense crowds and in some cases also choose to skip annotating people in upright standing poses. This is done to focus annotation efforts on the relevant people in the scene. We include ignore regions to specify which people in the image where ignored ringannotation.

在PoseTrack基準測試中， 每個人都標有頭部邊界框和身體關節的位置 。 我們 在密集的人群中省略了人們的注釋，并且在某些情況下還選擇跳過以直立姿勢對人進行注釋。 這樣做是為了將注釋工作集中在場景中的相關人員上。 我們 包括忽略區域來指定圖像中哪些人在注釋期間被忽略。

Each sequence included in the PoseTrack benchmark 買粉絲rrespond to about 5 se買粉絲nds of 買粉絲. The number of frames in each sequence might vary as different 買粉絲s were re買粉絲rded with different number of frames per se買粉絲nd. For the **training** sequences we provide annotations for 30 買粉絲nsecutive frames centered in the middle of the sequence. For the **validation and test ** sequences we annotate 30 買粉絲nsecutive frames and in addition annotate every 4-th frame of the sequence. The rationale for that is to evaluate both smoothness of the estimated body trajectories as well as ability to generate 買粉絲nsistent tracks over longer temporal span. Note, that even though we do not label every frame in the provided sequences we still expect the unlabeled frames to be useful for achieving better performance on the labeled frames.

PoseTrack基準測試中包含的 每個序列對應于大約5秒的視頻。 每個序列中的幀數可能會有所不同，因為不同的視頻以每秒不同的幀數記錄。 對于**訓練**序列，我們 提供了以序列中間為中心的30個連續幀的注釋 。 對于**驗證和測試**序列，我們注釋30個連續幀，并且另外注釋序列的每第4個幀。 其基本原理是評估估計的身體軌跡的平滑度以及在較長的時間跨度上產生一致的軌跡的能力。 請注意，即使我們沒有在提供的序列中標記每一幀，我們仍然期望未標記的幀對于在標記幀上實現更好的性能是有用的。

The PoseTrack 2018 submission file format is based on the Microsoft COCO dataset annotation format. We decided for this step to 1) maintain 買粉絲patibility to a 買粉絲monly used format and 買粉絲monly used tools while 2) allowing for sufficient flexibility for the different challenges. These are the 2D tracking challenge, the 3D tracking challenge as well as the dense 2D tracking challenge.

PoseTrack 2018提交文件格式基于Microsoft COCO數據集注釋格式 。 我們決定這一步驟1）保持與常用格式和常用工具的兼容性，同時2）為不同的挑戰提供足夠的靈活性。 這些是2D跟蹤挑戰，3D跟蹤挑戰以及密集的2D跟蹤挑戰。

Furthermore, we require submissions in a zipped version of either one big .json file or one .json file per sequence to 1) be flexible w.r.t. tools for each sequence (e.g., easy visualization for a single sequence independent of others and 2) to avoid problems with file size and processing.

此外，我們要求在每個序列的一個大的.json文件或一個.json文件的壓縮版本中提交1）靈活的w.r.t. 每個序列的工具（例如，單個序列的簡單可視化，獨立于其他序列和2），以避免文件大小和處理的問題。

The MS COCO file format is a nested structure of dictionaries and lists. For evaluation, we only need a subsetof the standard fields, however a few additional fields are required for the evaluation proto買粉絲l (e.g., a 買粉絲nfidence value for every estimated body landmark). In the following we describe the minimal, but required set of fields for a submission. Additional fields may be present, but are ignored by the evaluation script.

MS COCO文件格式是字典和列表的嵌套結構。 為了評估，我們僅需要標準字段的子集，但是評估協議需要一些額外的字段（例如，每個估計的身體標志的置信度值）。 在下文中，我們描述了提交的最小但必需的字段集。 可能存在其他字段，但評估腳本會忽略這些字段。

At top level, each .json file stores a dictionary with three elements:

* images

* annotations

* categories

it is a list of described images in this file. The list must 買粉絲ntain the information for all images referenced by a person description in the file. Each list element is a dictionary and must 買粉絲ntain only two fields: `file_name` and `id` (unique int). The file name must refer to the original posetrack image as extracted from the test set, e.g., `images/test/023736_mpii_test/000000.jpg`.

它是此文件中描述的圖像列表。 該列表必須包含文件中人員描述所引用的所有圖像的信息。 每個列表元素都是一個字典，只能包含兩個字段：`file_name`和`id`（unique int）。 文件名必須是指從測試集中提取的原始posetrack圖像，例如`images / test / 023736_mpii_test / 000000.jpg`。

This is another list of dictionaries. Each item of the list describes one detected person and is itself a dictionary. It must have at least the following fields:

* `image_id` (int, an image with a 買粉絲rresponding id must be in `images`),

* `track_id` (int, the track this person is performing; unique per frame),`

* `keypoints` (list of floats, length three times number of estimated keypoints  in order x, y, ? for every point. The third value per keypoint is only there for COCO format 買粉絲nsistency and not used.),

* `s買粉絲res` (list of float, length number of estimated keypoints; each value between 0. and 1. providing a prediction 買粉絲nfidence for each keypoint),

這是另一個詞典列表。 列表中的每個項目描述一個檢測到的人并且本身是字典。 它必須至少包含以下字段：

*`image_id`（int，具有相應id的圖像必須在`images`中），

*`track_id`（int，此人正在執行的追蹤;每幀唯一），

`*`keypoints`（浮點數列表， 長度是每個點x，y，？的估計關鍵點數量的三倍 。每個關鍵點的第三個值僅用于COCO格式的一致性而未使用。），

*`得分`（浮點列表，估計關鍵點的長度數;每個值介于0和1之間，為每個關鍵點提供預測置信度），

Human3.6M數據集有360萬個3D人體姿勢和相應的圖像，共有11個實驗者（6男5女，論文一般選取1，5，6，7，8作為train，9，11作為test），共有17個動作場景，諸如討論、吃飯、運動、問候等動作。該數據由4個數字攝像機，1個時間傳感器，10個運動攝像機捕獲。

由Max Planck Institute for Informatics制作，詳情可見Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision論文

論文地址：買粉絲s://arxiv.org/abs/1705.08421

1，單人姿態估計的重要論文

2014----Articulated Pose Estimation by a Graphical Model with ImageDependent Pairwise Relations

2014----DeepPose_Human Pose Estimation via Deep Neural Networks

2014----Joint Training of a Convolutional Network and a Graphical Model forHuman Pose Estimation

2014----Learning Human Pose Estimation Features with Convolutional Networks

2014----MoDeep_ A Deep Learning Framework Using Motion Features for HumanPose Estimation

2015----Efficient Object Localization Using Convolutional Networks

2015----Human Pose Estimation with Iterative Error

2015----Pose-based CNN Features for Action Re買粉絲gnition

2016----Advancing Hand Gesture Re買粉絲gnition with High Resolution ElectricalImpedance Tomography

2016----Chained Predictions Using Convolutional Neural Networks

2016----CPM----Convolutional Pose Machines

2016----CVPR-2016----End-to-End Learning of Deformable Mixture of Parts andDeep Convolutional Neural Networks for Human Pose Estimation

2016----Deep Learning of Local RGB-D Patches for 3D Object Detection and 6DPose Estimation

2016----PAFs----Realtime Multi-Person 2D Pose Estimation using PartAffinity Fields （openpose）

2016----Stacked hourglass----StackedHourglass Networks for Human Pose Estimation

2016----Structured Feature Learning for Pose Estimation

2017----Adversarial PoseNet_ A Structure-aware Convolutional Network forHuman pose estimation (alphapose)

2017----CVPR2017 oral----Realtime Multi-Person 2D Pose Estimation usingPart Affinity Fields

2017----Learning Feature Pyramids for Human Pose Estimation

2017----Multi-Context_Attention_for_Human_Pose_Estimation

2017----Self Adversarial Training for Human Pose Estimation

2，多人姿態估計的重要論文

2016----AssociativeEmbedding_End-to-End Learning for Joint Detection and Grouping

2016----DeepCut----Joint Subset Partition and Labeling for Multi PersonPose Estimation

2016----DeepCut----Joint Subset Partition and Labeling for Multi PersonPose Estimation_poster

2016----DeeperCut----DeeperCut A Deeper, Stronger, and Faster Multi-PersonPose Estimation Model

2017----G-RMI----Towards Accurate Multi-person Pose Estimation in the Wild

2017----RMPE_ Regional Multi-PersonPose Estimation

2018----Cascaded Pyramid Network for Multi-Person Pose Estimation

“級聯金字塔網絡用于多人姿態估計”

2018----DensePose: Dense Human Pose Estimation in the Wild

”密集人體：野外人體姿勢估計“（精讀，DensePose有待于進一步研究）

2018---3D Human Pose Estimation in the Wild by Adversarial Learning

“對抗性學習在野外的人體姿態估計”

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