Video Object Segmentation - Laura Leal-Taixé - UPC Barcelona 2018

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Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.

Prof. Laura Leal-Taixé
Dynamic Scene
Understanding

Dynamic Scene Understanding
Understand every pixel of a video

Dynamic Scene Understanding
Understand every pixel of a video
road
tree
Semantic
segmentation
person
car

Dynamic Scene Understanding
Understand every pixel of a video
person 1
tree
Semantic
segmentation
person 2
person 3
Instance-
based
segmentation
road
car

Dynamic Scene Understanding
Understand every pixel of a video
Semantic
segmentation
Instance-
based
segmentation
Multiple
object
tracking

Prof. Laura Leal-Taixé
Video Object
Segmentation without
Temporal Information

Input image First mask
Supervised video object segmentation

Architecture
• Series of convolutional filters
• Upsampling at different scales
• Final summation before prediction à get information from all scales
Boundary Snapping
Snap the foreground mask to accurate contours
3
Foreground Branch
Speciﬁc object - Less accurate contours
1
Contour Branch
Accurate contours - Generic objects
2

Architecture: improving boundaries
Boundary Snapping
Snap the foreground mask to accurate contours
3
Foreground Branch
Speciﬁc object - Less accurate contours
1
Contour Branch
Accurate contours - Generic objects
2

Architecture: improving boundaries
Select the
superpixels that
are 50% or more
covered by the
foreground mask
Boundary Snapping
Snap the foreground mask to accurate contours
3
Foreground Branch
Speciﬁc object - Less accurate contours
1
Contour Branch
Accurate contours - Generic objects
2

ResultsonframeN
oftestsequence
Base Network
Pre-trained on ImageNet
1
Parent Network
Trained on DAVIS training set
2
Test Network
Fine-tuned on frame 1 of test sequence
3
ResultsonframeN
oftestsequence
Base Network
Pre-trained on ImageNet
1
Parent Network
Trained on DAVIS training set
2
Test Network
Fine-tuned on frame 1 of test sequence
3
ResultsonframeN
oftestsequence
Base Network
Pre-trained on ImageNet
1
Parent Network
Trained on DAVIS training set
2
Test Network
Fine-tuned on frame 1 of test sequence
3
Edges and basic
image features
Learns how to
do video
segmentation
Learns which
object to
segment
One-Shot Video Object Segmentation
S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, L. van Gool. CVPR 2017
FinetuningPre-trained Training

102ms – Parent network
Finetuning time
DAVIS dataset
11.8 pp.

Experiments: heavy occlusions
Occlusion 1
Occlusion 2
First mask (input)

Introducing Semantics
• The network does not have the notion of objects
• Broken contours are often seen, especially when occluded parts
becomes visible

Semantic propagation
• OSVOS path: obtain a coarse foreground estimation
Semantic
Instance
Segmentation
Result
Top Matching InstancesInstance Proposals
Input Image
First-Round
Foreground
Estimation
Conditional
Classiﬁer
Semantic
Selection &
Propagation
Semantic Prior
Foreground Estimation
CNN
Appearance Model

Semantic propagation
• Semantic prior branch that gives us proposals to select from
Semantic
Instance
Segmentation
Result
Top Matching InstancesInstance Proposals
Input Image
First-Round
Foreground
Estimation
Conditional
Classiﬁer
Semantic
Selection &
Propagation
Semantic Prior
Foreground Estimation
CNN
Appearance Model

Semantic propagation
• Semantic prior branch that gives us proposals to select from
• Enforce that semantics stay coherent throughout the sequence
Semantic
Instance
Segmentation
Result
Top Matching InstancesInstance Proposals
Input Image
First-Round
Foreground
Estimation
Conditional
Classiﬁer
Semantic
Selection &
Propagation
Semantic Prior
Foreground Estimation
CNN
Appearance Model

Semantic propagation
Semantic Selection
Selected Instances:
Person and Motorbike
Ground Truth
Instance Segmentation Proposals
Semantic Propagation
InstanceSegmentation
Proposals
First-Round
ForegroundEstimation
TopPersonand
Motorbike
Frame 0 Frame 18 Frame 24 Frame 30 Frame 36

Ablation studies on DAVIS 2016
Finetuning on the first mask

Ablation studies on DAVIS 2016
Pre-training the parent network

Ablation studies on DAVIS 2016
Adding semantic information

Ablation studies on DAVIS 2016
Brings a substantial boost in temporal stability

Conclusions
• We can do consistent video object segmentation without the need
of temporal information
• Pose changes show parts of the object the network has not seen
and therefore holes in the result are created
• Semantic priors help in delivering consistent object-type
segmentations

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Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...

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M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years. Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success. MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies. According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”

Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation

Boston Institute of Analytics

Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

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一比一原版(UofS毕业证书)萨省大学毕业证如何办理

v3tuleee

原版定制【微信:41543339】【(UofS毕业证书)萨省大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Opendatabay - Open Data Marketplace.pptx

Opendatabay

Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets. First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience. From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets. Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.

一比一原版(UVic毕业证)维多利亚大学毕业证成绩单

ukgaet

UVic毕业证【微信95270640】（维多利亚大学毕业证成绩单本科学历）Q微信95270640(补办UVic学位文凭证书)维多利亚大学留信网学历认证怎么办理维多利亚大学毕业证成绩单精仿本科学位证书硕士文凭证书认证Seneca College diplomaoffer,Transcript办理硕士学位证书造假维多利亚大学假文凭学位证书制作UVic本科毕业证书硕士学位证书精仿维多利亚大学学历认证成绩单修改制作，办理真实认证、留信认证、使馆公证、购买成绩单，购买假文凭，购买假学位证，制造假国外大学文凭、毕业公证、毕业证明书、录取通知书、Offer、在读证明、雅思托福成绩单、假文凭、假毕业证、请假条、国际驾照、网上存档可查！【实体公司】办维多利亚大学维多利亚大学毕业证文凭证书学历认证学位证文凭认证办留信网认证办留服认证办教育部认证（网上可查实体公司专业可靠） — — — 留学归国服务中心 — — - 【主营项目】一.维多利亚大学毕业证成绩单使馆认证教育部认证成绩单等！二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 国外毕业证学位证成绩单办理流程： 1客户提供维多利亚大学维多利亚大学毕业证文凭证书办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 2开始安排制作毕业证成绩单电子图； 3毕业证成绩单电子版做好以后发送给您确认； 4毕业证成绩单电子版您确认信息无误之后安排制作成品； 5成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。专业服务请勿犹豫联系我！本公司是留学创业和海归创业者们的桥梁。一次办理终生受用一步到位高效服务。详情请在线咨询办理,欢迎有诚意办理的客户咨询!洽谈。招聘代理：本公司诚聘英国加拿大澳洲新西兰美国法国德国新加坡各地代理人员如果你有业余时间有兴趣就请联系我们咨询顾问：+微信:95270640刀劈开抑或用拳头砸开每人抱起一大块就啃啃得满嘴满脸猴屁股般的红艳大家一个劲地指着对方吃吃地笑瓜裂得古怪奇形怪状却丝毫不影响瓜味甜丝丝的满嘴生津遍地都是瓜横七竖八的活像掷满了一地的大石块摘走二三只爷爷是断然发现不了的即便发现爷爷也不恼反而教山娃辨认孰熟孰嫩孰甜孰淡名义上是护瓜往往在瓜棚里坐上一刻饱吃一顿后山娃就领着阿黑漫山遍野地跑阿黑是一条黑色的大猎狗挺机灵的是山娃多年的忠实伙伴平时山娃上学阿黑也静

Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...

John Andrews

SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation" Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults Description: Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project. Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas

The affect of service quality and online reviews on customer loyalty in the E...

jerlynmaetalle

【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】

NABLAS株式会社

一比一原版(UniSA毕业证书)南澳大学毕业证如何办理

slg6lamcq

原版定制【微信:41543339】【(UniSA毕业证书)南澳大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

社内勉強会資料_LLM Agents　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　.

NABLAS株式会社

一比一原版(CBU毕业证)卡普顿大学毕业证如何办理

ahzuo

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ArpitMalhotra16

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Subhajit Sahu

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

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一比一原版(CBU毕业证)卡普顿大学毕业证如何办理

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Video Object Segmentation - Laura Leal-Taixé - UPC Barcelona 2018

1. Prof. Laura Leal-Taixé Dynamic Scene Understanding

2. Dynamic Scene Understanding Understand every pixel of a video

3. Dynamic Scene Understanding Understand every pixel of a video road tree Semantic segmentation person car

4. Dynamic Scene Understanding Understand every pixel of a video person 1 tree Semantic segmentation person 2 person 3 Instance- based segmentation road car

5. Dynamic Scene Understanding Understand every pixel of a video Semantic segmentation Instance- based segmentation Multiple object tracking

6. Prof. Laura Leal-Taixé Video Object Segmentation without Temporal Information

7. Input image First mask Supervised video object segmentation

8. Architecture • Series of convolutional filters • Upsampling at different scales • Final summation before prediction à get information from all scales Boundary Snapping Snap the foreground mask to accurate contours 3 Foreground Branch Speciﬁc object - Less accurate contours 1 Contour Branch Accurate contours - Generic objects 2

9. Architecture: improving boundaries Boundary Snapping Snap the foreground mask to accurate contours 3 Foreground Branch Speciﬁc object - Less accurate contours 1 Contour Branch Accurate contours - Generic objects 2

10. Architecture: improving boundaries Boundary Snapping Snap the foreground mask to accurate contours 3 Foreground Branch Speciﬁc object - Less accurate contours 1 Contour Branch Accurate contours - Generic objects 2

11. Architecture: improving boundaries Select the superpixels that are 50% or more covered by the foreground mask Boundary Snapping Snap the foreground mask to accurate contours 3 Foreground Branch Speciﬁc object - Less accurate contours 1 Contour Branch Accurate contours - Generic objects 2

12. ResultsonframeN oftestsequence Base Network Pre-trained on ImageNet 1 Parent Network Trained on DAVIS training set 2 Test Network Fine-tuned on frame 1 of test sequence 3 ResultsonframeN oftestsequence Base Network Pre-trained on ImageNet 1 Parent Network Trained on DAVIS training set 2 Test Network Fine-tuned on frame 1 of test sequence 3 ResultsonframeN oftestsequence Base Network Pre-trained on ImageNet 1 Parent Network Trained on DAVIS training set 2 Test Network Fine-tuned on frame 1 of test sequence 3 Edges and basic image features Learns how to do video segmentation Learns which object to segment One-Shot Video Object Segmentation S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, L. van Gool. CVPR 2017 FinetuningPre-trained Training

13. ResultsonframeN oftestsequence Base Network Pre-trained on ImageNet 1 Parent Network Trained on DAVIS training set 2 Test Network Fine-tuned on frame 1 of test sequence 3 Learns which object to segment One-Shot Video Object Segmentation • Learning the appearance of the foreground and background objects Finetuning

14. 102ms – Parent network Finetuning time DAVIS dataset 11.8 pp.

15. Experiments: heavy occlusions Occlusion 1 Occlusion 2 First mask (input)

16. Experiments: number of annotations

17. Experiments: number of annotations

18. Experiments: highly dynamic scenes

19. Introducing Semantics • The network does not have the notion of objects • Broken contours are often seen, especially when occluded parts becomes visible

20. Introducing Semantics

21. Introducing Semantics

22. Semantic propagation • OSVOS path: obtain a coarse foreground estimation Semantic Instance Segmentation Result Top Matching InstancesInstance Proposals Input Image First-Round Foreground Estimation Conditional Classiﬁer Semantic Selection & Propagation Semantic Prior Foreground Estimation CNN Appearance Model

23. Semantic propagation • Semantic prior branch that gives us proposals to select from Semantic Instance Segmentation Result Top Matching InstancesInstance Proposals Input Image First-Round Foreground Estimation Conditional Classiﬁer Semantic Selection & Propagation Semantic Prior Foreground Estimation CNN Appearance Model

24. Semantic propagation • Semantic prior branch that gives us proposals to select from • Enforce that semantics stay coherent throughout the sequence Semantic Instance Segmentation Result Top Matching InstancesInstance Proposals Input Image First-Round Foreground Estimation Conditional Classiﬁer Semantic Selection & Propagation Semantic Prior Foreground Estimation CNN Appearance Model

25. Semantic propagation Semantic Selection Selected Instances: Person and Motorbike Ground Truth Instance Segmentation Proposals Semantic Propagation InstanceSegmentation Proposals First-Round ForegroundEstimation TopPersonand Motorbike Frame 0 Frame 18 Frame 24 Frame 30 Frame 36

26. Semantic propagation Semantic Selection Selected Instances: Person and Motorbike Ground Truth Instance Segmentation Proposals Semantic Propagation InstanceSegmentation Proposals First-Round ForegroundEstimation TopPersonand Motorbike Frame 0 Frame 18 Frame 24 Frame 30 Frame 36

27. Semantic propagation Semantic Selection Selected Instances: Person and Motorbike Ground Truth Instance Segmentation Proposals Semantic Propagation InstanceSegmentation Proposals First-Round ForegroundEstimation TopPersonand Motorbike Frame 0 Frame 18 Frame 24 Frame 30 Frame 36

28. Semantic propagation Semantic Selection Selected Instances: Person and Motorbike Ground Truth Instance Segmentation Proposals Semantic Propagation InstanceSegmentation Proposals First-Round ForegroundEstimation TopPersonand Motorbike Frame 0 Frame 18 Frame 24 Frame 30 Frame 36

29. Semantic propagation Semantic Selection Selected Instances: Person and Motorbike Ground Truth Instance Segmentation Proposals Semantic Propagation InstanceSegmentation Proposals First-Round ForegroundEstimation TopPersonand Motorbike Frame 0 Frame 18 Frame 24 Frame 30 Frame 36

30. Ablation studies on DAVIS 2016 Finetuning on the first mask

31. Ablation studies on DAVIS 2016 Pre-training the parent network

32. Ablation studies on DAVIS 2016 Adding semantic information

33. Ablation studies on DAVIS 2016 Adding semantic information

34. Ablation studies on DAVIS 2016 Brings a substantial boost in temporal stability

35. Conclusions • We can do consistent video object segmentation without the need of temporal information • Pose changes show parts of the object the network has not seen and therefore holes in the result are created • Semantic priors help in delivering consistent object-type segmentations

36. Thank you Dr. Laura Leal-Taixé

Video Object Segmentation - Laura Leal-Taixé - UPC Barcelona 2018

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