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    <title>Computer Vision Group teaching:ws2023</title>
    <subtitle></subtitle>
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    <updated>2026-04-23T03:47:34+00:00</updated>
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    <entry>
        <title>Master Seminar: 3D Shape Matching and Application in Computer Vision (5 ECTS)</title>
        <link rel="alternate" type="text/html" href="https://cvg.cit.tum.de/teaching/ws2023/3dsm?rev=1691742402&amp;do=diff"/>
        <published>2023-08-11T08:26:42+00:00</published>
        <updated>2023-08-11T08:26:42+00:00</updated>
        <id>https://cvg.cit.tum.de/teaching/ws2023/3dsm?rev=1691742402&amp;do=diff</id>
        <author>
            <name>Anonymous</name>
            <email>anonymous@undisclosed.example.com</email>
        </author>
        <category  term="teaching:ws2023" />
        <content>----------

Master Seminar: 3D Shape Matching and Application in Computer Vision (5 ECTS)

 Winter Semester 2023/24, TU München 

Organisers: 
Maolin Gao, Viktoria Ehm

2023-07-07: [preliminary meeting] will take place hybrid at 10:00 - 11:00 on 12.07.2023 in room 02.09.023 or over zoom. The slides will be published afterwards. You are encouraged to participate and ask questions directly in the meeting, since it will positively affect the matching process. Please find the zoom link in TUMonline.…</content>
        <summary>----------

Master Seminar: 3D Shape Matching and Application in Computer Vision (5 ECTS)

 Winter Semester 2023/24, TU München 

Organisers: 
Maolin Gao, Viktoria Ehm

2023-07-07: [preliminary meeting] will take place hybrid at 10:00 - 11:00 on 12.07.2023 in room 02.09.023 or over zoom. The slides will be published afterwards. You are encouraged to participate and ask questions directly in the meeting, since it will positively affect the matching process. Please find the zoom link in TUMonline.…</summary>
    </entry>
    <entry>
        <title>Practical Course: Creation of Deep Learning Methods (10 ECTS)</title>
        <link rel="alternate" type="text/html" href="https://cvg.cit.tum.de/teaching/ws2023/create_dl?rev=1707386437&amp;do=diff"/>
        <published>2024-02-08T10:00:37+00:00</published>
        <updated>2024-02-08T10:00:37+00:00</updated>
        <id>https://cvg.cit.tum.de/teaching/ws2023/create_dl?rev=1707386437&amp;do=diff</id>
        <author>
            <name>Anonymous</name>
            <email>anonymous@undisclosed.example.com</email>
        </author>
        <category  term="teaching:ws2023" />
        <content>----------

Practical Course: Creation of Deep Learning Methods (10 ECTS)

 Winter Semester 2023/24, TU Munich 


here.


Please send applications (including learning goals, programming skills description, code, all grade transcripts (small PDF file size) - see preliminary-meeting slides) to create-dl[at]vision.in.tum.de</content>
        <summary>----------

Practical Course: Creation of Deep Learning Methods (10 ECTS)

 Winter Semester 2023/24, TU Munich 


here.


Please send applications (including learning goals, programming skills description, code, all grade transcripts (small PDF file size) - see preliminary-meeting slides) to create-dl[at]vision.in.tum.de</summary>
    </entry>
    <entry>
        <title>Computer Vision III: Detection, Segmentation and Tracking</title>
        <link rel="alternate" type="text/html" href="https://cvg.cit.tum.de/teaching/ws2023/cv3?rev=1697733354&amp;do=diff"/>
        <published>2023-10-19T16:35:54+00:00</published>
        <updated>2023-10-19T16:35:54+00:00</updated>
        <id>https://cvg.cit.tum.de/teaching/ws2023/cv3?rev=1697733354&amp;do=diff</id>
        <author>
            <name>Anonymous</name>
            <email>anonymous@undisclosed.example.com</email>
        </author>
        <category  term="teaching:ws2023" />
        <content>----------

Computer Vision III: Detection, Segmentation and Tracking

Overview

The &quot;Computer Vision III&quot; course in the winter semester 23/24 provides a comprehensive overview of methods for high-level computer vision tasks: object detection, object/scene segmentation and (multi-)object tracking.
These tasks are one of the most compelling applications of computer vision in the real world.
Despite many challenges, we have seen substantial progress over the past years, fuelled by deep learning. 
…</content>
        <summary>----------

Computer Vision III: Detection, Segmentation and Tracking

Overview

The &quot;Computer Vision III&quot; course in the winter semester 23/24 provides a comprehensive overview of methods for high-level computer vision tasks: object detection, object/scene segmentation and (multi-)object tracking.
These tasks are one of the most compelling applications of computer vision in the real world.
Despite many challenges, we have seen substantial progress over the past years, fuelled by deep learning. 
…</summary>
    </entry>
    <entry>
        <title>Practical Course: Expert-Level Deep Learning (10 ECTS)</title>
        <link rel="alternate" type="text/html" href="https://cvg.cit.tum.de/teaching/ws2023/dlpractice?rev=1707386468&amp;do=diff"/>
        <published>2024-02-08T10:01:08+00:00</published>
        <updated>2024-02-08T10:01:08+00:00</updated>
        <id>https://cvg.cit.tum.de/teaching/ws2023/dlpractice?rev=1707386468&amp;do=diff</id>
        <author>
            <name>Anonymous</name>
            <email>anonymous@undisclosed.example.com</email>
        </author>
        <category  term="teaching:ws2023" />
        <content>----------

Practical Course: Expert-Level Deep Learning (10 ECTS)

 Winter Semester 2023/24, TU Munich 


here.


Please send applications (including learning goals, programming skills description, code, all grade transcripts (small PDF file size) - see preliminary-meeting slides) to dlpractice[at]vision.in.tum.de</content>
        <summary>----------

Practical Course: Expert-Level Deep Learning (10 ECTS)

 Winter Semester 2023/24, TU Munich 


here.


Please send applications (including learning goals, programming skills description, code, all grade transcripts (small PDF file size) - see preliminary-meeting slides) to dlpractice[at]vision.in.tum.de</summary>
    </entry>
    <entry>
        <title>Seminar: Foundational Models for 2D and 3D Computer Vision (5 ECTS)</title>
        <link rel="alternate" type="text/html" href="https://cvg.cit.tum.de/teaching/ws2023/fmcv?rev=1690999805&amp;do=diff"/>
        <published>2023-08-02T18:10:05+00:00</published>
        <updated>2023-08-02T18:10:05+00:00</updated>
        <id>https://cvg.cit.tum.de/teaching/ws2023/fmcv?rev=1690999805&amp;do=diff</id>
        <author>
            <name>Anonymous</name>
            <email>anonymous@undisclosed.example.com</email>
        </author>
        <category  term="teaching:ws2023" />
        <content>----------

Seminar: Foundational Models for 2D and 3D Computer Vision (5 ECTS)

 Winter Semester 2023/24, TU München 

Organisers: 
Dominik Schnaus, Tarun Yenamandra

Please direct questions to &lt;fmcv-ws23@cvpr.in.tum.de&gt;

Course Materials:
Course Materials (password protected; please email us or look in the preliminary meeting slides for the password)</content>
        <summary>----------

Seminar: Foundational Models for 2D and 3D Computer Vision (5 ECTS)

 Winter Semester 2023/24, TU München 

Organisers: 
Dominik Schnaus, Tarun Yenamandra

Please direct questions to &lt;fmcv-ws23@cvpr.in.tum.de&gt;

Course Materials:
Course Materials (password protected; please email us or look in the preliminary meeting slides for the password)</summary>
    </entry>
    <entry>
        <title>Seminar: Advanced topics in Graph Learning (5 ECTS)</title>
        <link rel="alternate" type="text/html" href="https://cvg.cit.tum.de/teaching/ws2023/graphlearning_ws2023?rev=1690999827&amp;do=diff"/>
        <published>2023-08-02T18:10:27+00:00</published>
        <updated>2023-08-02T18:10:27+00:00</updated>
        <id>https://cvg.cit.tum.de/teaching/ws2023/graphlearning_ws2023?rev=1690999827&amp;do=diff</id>
        <author>
            <name>Anonymous</name>
            <email>anonymous@undisclosed.example.com</email>
        </author>
        <category  term="teaching:ws2023" />
        <content>Seminar: Advanced topics in Graph Learning (5 ECTS)

Winter Semester 2023/2024, TU München

Organizers: 
 Christian Koke,  Abhishek Saroha

Correspondence

Please forward any queries related to the seminar to: &lt;Abhishek.Saroha@in.tum.de&gt;.


Course Registration

Assignment to the lab is done via the  matching system. In addition to applying to the matching system please remember to also send in your application (with current transcripts) via e-mail at the latest by 19 July 2023.</content>
        <summary>Seminar: Advanced topics in Graph Learning (5 ECTS)

Winter Semester 2023/2024, TU München

Organizers: 
 Christian Koke,  Abhishek Saroha

Correspondence

Please forward any queries related to the seminar to: &lt;Abhishek.Saroha@in.tum.de&gt;.


Course Registration

Assignment to the lab is done via the  matching system. In addition to applying to the matching system please remember to also send in your application (with current transcripts) via e-mail at the latest by 19 July 2023.</summary>
    </entry>
    <entry>
        <title>Seminar: Neural Network Design Patterns in Computer Vision (5 ECTS)</title>
        <link rel="alternate" type="text/html" href="https://cvg.cit.tum.de/teaching/ws2023/seminar_design_patterns?rev=1714748994&amp;do=diff"/>
        <published>2024-05-03T15:09:54+00:00</published>
        <updated>2024-05-03T15:09:54+00:00</updated>
        <id>https://cvg.cit.tum.de/teaching/ws2023/seminar_design_patterns?rev=1714748994&amp;do=diff</id>
        <author>
            <name>Anonymous</name>
            <email>anonymous@undisclosed.example.com</email>
        </author>
        <category  term="teaching:ws2023" />
        <content>----------

Seminar: Neural Network Design Patterns in Computer Vision (5 ECTS)

 Winter Semester 2023/24, TU München 

Organiser: 
Roman Pflugfelder

Description

Computer vision considers models of neural networks originally invented by the machine learning community. This seminar allows the student to study neural networks in more detail. By the end of the seminar, all participants will understand the principles and the applications of selected models, and they will receive the ability to reu…</content>
        <summary>----------

Seminar: Neural Network Design Patterns in Computer Vision (5 ECTS)

 Winter Semester 2023/24, TU München 

Organiser: 
Roman Pflugfelder

Description

Computer vision considers models of neural networks originally invented by the machine learning community. This seminar allows the student to study neural networks in more detail. By the end of the seminar, all participants will understand the principles and the applications of selected models, and they will receive the ability to reu…</summary>
    </entry>
    <entry>
        <title>Seminar: The Evolution of Motion Estimation and Real-time 3D Reconstruction</title>
        <link rel="alternate" type="text/html" href="https://cvg.cit.tum.de/teaching/ws2023/seminar_realtime3d?rev=1697550759&amp;do=diff"/>
        <published>2023-10-17T13:52:39+00:00</published>
        <updated>2023-10-17T13:52:39+00:00</updated>
        <id>https://cvg.cit.tum.de/teaching/ws2023/seminar_realtime3d?rev=1697550759&amp;do=diff</id>
        <author>
            <name>Anonymous</name>
            <email>anonymous@undisclosed.example.com</email>
        </author>
        <category  term="teaching:ws2023" />
        <content>----------

Seminar: The Evolution of Motion Estimation and Real-time 3D Reconstruction

Seminar IN2107, which is open to Master students from &quot;computer science&quot;, &quot;games engineering&quot;, &quot;robotics, cognition, and intelligence&quot;, and other programs. Please consult TUMonline or your study coordinator for more information.</content>
        <summary>----------

Seminar: The Evolution of Motion Estimation and Real-time 3D Reconstruction

Seminar IN2107, which is open to Master students from &quot;computer science&quot;, &quot;games engineering&quot;, &quot;robotics, cognition, and intelligence&quot;, and other programs. Please consult TUMonline or your study coordinator for more information.</summary>
    </entry>
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