CVL Seminars

The CVL Seminar series consist of scientific talks about various topics of computer vision, visualization and medical image analysis. The oral presentations range from Semester and Master Thesis presentation, through presentations of CVL lab members (such as conference talk rehearsals) to talks given by invited speakers.

Date and time: The seminars are usually on Thursday at 10:30am in ETF C106, please contact Dr. Danda Pani Paudel to schedule a talk.

Timing: 20 min + 10 min questions for Semester and Master works, 45 min + 15 min questions for others. Semester and Master works will have a strict timing, although there might be further discussion after the "official" time.

Mailing list: The [cvl-seminar] mailing list informs you about all public upcoming events. Subscribe here. Reminders of seminars with full details will be emailed to all those on our seminar mailing list (this automatically includes all CVL Group members and students currently doing a Semester or Master Thesis with us).

Calendar of events: Please find below the calendar with all scheduled seminars. You can subscribe to the calendar using the provided links.


Links: ICAL , HTML

Comments to: Dr. Danda Pani Paudel.

List of events: Click on the links for specific details of the seminars.

Date and time Place Speaker Title of the talk
March 7, 2019, 10 a.m. ETF C106 Prof. Paolo Favaro

Computer Vision Group,
Universität Bern, Switzerland

Towards Unsupervised Learning
Feb. 26, 2019, 11 a.m. ETF C109 Hlynur Skulason

Semester Work
Supervisors: Dr. med. Anton S. Becker and Prof. Dr. Ender Konukoglu

Using Deep Neural Networks to Correct View-Angle Deviations in Wrist X-Ray Assessment
Feb. 21, 2019, 3 p.m. ETF C106 Jiayu Chen

Semester Work
Supervisors: - Prof. Ender Konukoglu, Prof. Gunnar Ratsch, Vincent Fortuin

Adversarial Attacks on Interpretation Models
Feb. 19, 2019, 10 a.m. ETF C106 Ozan Uenal

Semester Work
Supervisors: Dr. Richard Rau, Prof. Orçun Göksel

Reflector-based Ultrasound Attenuation Imaging
Feb. 18, 2019, 2 p.m. ETF C106 Siwei Zhang

Semester Work
Supervisors: Yuhua Chen, Xiaoran Chen, Prof. Luc Van Gool

A study of Differences between Semantic Segmentation and Monocular Depth Estimation via Shared CNN Architecture
Feb. 18, 2019, 11 a.m. ETF C106 Berk Dogan

Semester Work
Supervisors: Firat Ozdemir, Prof. Orcun Goksel

Muscle Agnostic Musculoskeletal Segmentation with Adversarial Networks
Feb. 18, 2019, 10:15 a.m. ETF C106 Julian Hengsteler

Semester Work
Supervisors: Dr. Richard Rau, Prof. Orçun Göksel

Continuous Domain Ultrasound Scatterer Reconstruction
Feb. 15, 2019, 3 p.m. ETF C106 Orhun Caner Eren

Semester Work
Supervisors: Dr. Danda Pani Paudel and Prof. Luc Van Gool

Persistence Diagrams for 3D Point Cloud Classification
Feb. 15, 2019, 2:30 p.m. ETF C106 Kangning Liu

Semester Work
Supervisors: Dr. Wen Li, Yuhua Chen, and Prof. Luc Van Gool

Improving Cross-domain Semantic Segmentation Performance by Mining Weak Paired Information
Feb. 15, 2019, 2 p.m. ETF C106 Haoran Wang

Semester Work
Supervisors: Dr. Wen Li, Yuhua Chen, and Prof. Luc Van Gool

Multi-scale Domain Adaptive Faster R-CNN for robust cross-domain object detection
Feb. 14, 2019, 3:30 p.m. ETF C106 Yardim, Ali Batuhan

Semester Work
Supervisors: Yuhua Chen, Xiaoran Chen, Prof. Luc Van Gool

Using Pixel-wise Embeddings for Efficient Interactive Image Segmentation
Feb. 14, 2019, 3 p.m. ETF C106 Mike Hao JIANG

Master Thesis
Supervisors: Prof. Luc Van Gool, Prof. Andrew B. Lippman (MIT)

Enlightened: Broaden Your Views
Feb. 7, 2019, 11 a.m. ETF C106 Meva Himmetoglu

Semester Work
Supervisors: Yuhua Chen, Stamatios Georgoulis

Model Fusion on Deep Semantic Image Segmentation for Urban Street Scenes
Feb. 7, 2019, 10:30 a.m. ETF C106 Manuel Fritsche

Semester Work
Supervisors: Yuhua Chen, Stamatios Georgoulis

Semi-supervised Learning of Semantic Segmentation from Video
Feb. 1, 2019, 2 p.m. ETF-C106 Yichuan Wang

Semester Work
Supervisors: Dr. Zhiwu Huang, Dr. Danda Pani Paudel, Prof. Luc Van Gool

Benchmarking Per-frame Enhancement Methods over Low-quality Videos from Internet and Mobile Phones