4th Summer School

on Deep Learning for Computer Vision


UPC ETSETB TelecomBCN (June 25 - July 1, 2019)

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.


Open Lectures by Guest Speakers

This Summer School will include two open talks from on Aula Master of A3 building in Campus Nord UPC (directions). This session will be open to the general public.

Syllabus (draft)

Tuesday, June 25 @ D5-010

  • 15:00 D1L1 Welcome (XG)
  • 15:25 D1L2 Neural Network Zoo (XG)
  • 15:50 D1L3 Image Classification (KM)
  • 16:15 Break
  • 16:35 D1L4 Image Retrieval (XG)
  • 17:00 D1L5 Object detection I (MB)
  • 17:25 D1L6 Object detection II (MB)
  • 18:00 Project (KM, XG, AD, DF, AG & FR)
  • 19:00 End of the day

Wednesday, June 26 @ D5-010

  • 15:00 D2L1 Face detection & recognition (ES)
  • 15:25 D2L2 Medical imaging @ DCU (KM)
  • 15:50 D2L3 Medical imaging @ UPC (ES)
  • 16:15 Break
  • 16:35 D2L4 Semantic segmentation (MB)
  • 17:00 D2L5 Instance Segmentation (MB)
  • 17:25 D2L6 Intepretability (XG)
  • 18:00 Project (KM, XG, AD, DF, AG & FR)
  • 19:00 End of the day


Thursday, June 27 @ D5-010

  • 10:00 D3L1&2 Video analysis (VC)
  • 10:50 D3L3 Object tracking (AG)
  • 11:15 Break
  • 11:35 D3L4 Video Semantic Segmentation (CV)
  • 12:00 D3L5 Video Object Segmentation (CV)
  • 12:25 D3L6 Saliency Prediction (KM)
  • 13:00 Project (KM, XG, EM, DF, AG & FR)
  • 14:00 End of the day

Friday, June 28 @ D5-010

  • 10:00 D4L1 3D Analysis (JR)
  • 10:25 D4L2 3D Reconstruction (ER)
  • 10:50 D4L3 Generative models (KM)
  • 11:15 Break
  • 11:35 D4L4 Language and Vision (XG)
  • 12:00 D4L5 Audio and vision (AD)
  • 12:25 D4L6 Speech and vision (XG)
  • 13:00 Project (KM, XG, EM, DF, AG & FR)
  • 14:00 End of the day

Monday, July 1 @ A3-Aula Master (Open Day,)

  • 15:00 Student project expos
  • 16:30 Research posters session.
  • 17:30 Guest Lecture: TBD
  • 18:10 Guest Lecture: TBD
  • 18:50 Closing & homework (XG)

Check the pictures from the 2017 edition.



  • Course codes: 230360 (Master)
  • ECTS credits: 2.5 (corresponds to full-time dedication during the week course)
  • Teaching language: English
  • The course is offered for both master and bachelor students, but under two study programmes adapted to each profile.
  • Class Dates: June 25 - July 1, 2018
  • Class Schedule: 3pm-7pm (you will need 4 extra hours a day for homework during the week course)
  • Capacity: 40 students
  • Location: Campus Nord UPC, Module D5, Room 010


This Summer School requires a previous knowledge on basic deep learning techniques, which will not be covered. Please follow these indications depending on your profile:

If you have no previous experience on deep learning:

You should not sign up for this course. Build a strong basis whether by attending to our Winter School on Introduction to Deep Learning at the end of January 2019, or signing up for the full master course of Deep Learning for Artificial Intelligence to be taught during Autumn 2018.

If you have taken a previous edition of DLAI, DLCV, DLSL or have previous experience on deep learning:

  • Master students at ETSETB: Registration is available from the ETSETB academic office. There is an extraordinary registration around the end of May. You can request it from esecretaria and choose “Procedures > related to enrollment”. There you must choose “Enrollment change” or “add a subject”. Only requests made this way will be taken into account.

  • Bachelor (grau) students at ETSETB: You can register these credits as “conjunt d’activitats d’extensió universitària”. You will need to first register through this form before 10 June 2019. After the course sign up for the credits at the ETSETB academic office.

  • CFIS students: Register directly at the CFIS academic office.

  • Master students at FIB: Contact the FIB academic office. They will collect all applications and submit them to ETSETB for approval.

  • Other students at UPC & Master in Computer Vision: You might audit the course, with no official certification. If interested, fill in this form before 10 June 2019. You may be granted a seat, if there are any available after ETSETB, CFIS & FIB students have signed up.

  • Other students at European centres in the Erasmus+ network: You can register to the course following the normal procedures of the Erasmus program. Contact the your home international office to know the details that apply to your specific case.

  • Local industry members and other students from the European Higher Education Area: You must apply for being accepted in the course and cover the 100% cost of the ECTS credits, without the support of the public funds. This corresponds to 143,08 € per ECTS credit (Summer 2016). You may have a seat, if there are any available after UPC students have signed up.If you are interested in this option, please contact send an e-mail to secretaria@etsetb.upc.edu with subject “Inscripció al seminari MET 230360 DLCV”, attaching single page motivation letter stating your previous experience with deep learning, as well as a one page CV, both in PDF. Registration is limited to local industry members and students registered in an institutionfrom the European Higher Education Area.


Piazza will be used for class discussion and communication, instead of the regular UPC Atenea platform. Piazza is highly catered to getting you help fast and efficiently from classmates, TAs and instructors. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com.

Find us at the class page.


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