February 27, 2021, 11 CET

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Computer Vision Talks

by Visage Technologies

The registrations are now closed.

Computer vision in practice: tips, tricks and research

Computer Vision Talks by Visage Technologies is a free online conference for computer vision professionals and enthusiasts, designed to inspire and educate.

Dive deep into quality technical sessions covering the latest innovations in computer vision, interesting use cases, key concepts, best practices, and more. Walk away with new perspectives and insights you can implement into your own projects, and continue the discussions with your new connections after the event.


Learn how you can use computer vision to drive better experiences and streamline operations in concise, 30-minute sessions led by computer vision experts.


Each speaker will be available for questions after their presentation. Listen along, raise any burning questions you might have, and get live answers.


This is a completely online event. Tune in from your favorite spot, choose your own dress code, and enjoy live, top-notch sessions in a cozy, relaxed atmosphere.


Steep organic growth has positioned Visage Technologies among the fastest-growing tech companies for 4 years in a row. This growth is based on the expertise of Visage Technologies’ team that has, so far, developed two software development kits, delivered dozens of custom solutions, and created computer vision algorithms for some of the world’s most advanced cars. Join some of the team’s best engineers as they share the most valuable tips and experiences in interactive, live sessions.

Ivan Gogic
Ivan Gogić
Director of Research and Development
Petar Stojanac
Head of Face Technology Division
Vjekoslav Ranogajec
Vjekoslav Ranogajec
Director of Solutions and Custom Development
Vedran Pamukovic
Vedran Pamuković
Senior Research & Development Engineer
Lea Alfier
Lea Alfier
Software Engineer
Lana Periša
Research and Development Engineer
Nikola Mrzljak
Research and Development Engineer
Jure Pajic
Jure Pajić
Research and Development Engineer
Filip Soldan
Filip Soldan
Software Engineer in Test
Anamarija Čavka
Data and Machine Learning Specialist


Enhance your professional skills through interactive sessions covering a selection of contemporary computer vision topics – from tensors and inference engines to testing automation and interesting use cases. Attend live sessions, ask questions virtually, and get access to recorded sessions after the conference.

Face alignment is the process of determining a face shape given its location and size in an image. It’s used as a basis for other facial analysis tasks, human-machine interaction, and augmented reality applications. It’s also a challenging problem due to the extremely high variability in facial appearance affected by many external (illumination, occlusion, head pose) and internal factors (race, facial expression).

This session will present the latest regression-based approaches, recent training techniques, and a benchmark comparison of the most successful methods.

Speaker: Ivan Gogić, Director of Research and Development

Deep neural networks are the backbone of important algorithms implemented in the SDK for tracking, analyzing and recognizing faces developed at Visage Technologies. Since the SDK is designed to run efficiently on a variety of platforms (from Windows, Linux, and Mac OS to Android, iOS, and a variety of embedded systems), we need adequate inference engine mechanisms to enable deep network execution. Examples of such mechanisms are OpenVINO, TFLite, NCNN, and Visage Technologies’ in-house inference engine.

This session will provide an overview of inference mechanisms and our experience of their integration under a common API.

Speakers: Nikola Mrzljak & Jure Pajić, R&D Engineers

How can we ensure that a new iteration of a tracking algorithm is better than the previous one? Moreover, how can we automate the process to free up the resources for developing new features?

Learn how we do this and more in Visage Technologies in this session.

Speaker: Petar Stojanac, Head of Face Technology Division

As a part of the project with a pharmaceutical company, Visage Technologies created Maria, a virtual doctor. Maria is essentially a chatbot that moves, speaks and reacts to your emotions and your body mass index estimation in a predefined way. This means that she is more of an NPC in a video game than an intelligent agent. However, she is very flexible in what she does, which can be summarized as content-agnostic, question-answer based conversations. This was achieved via a configurable architecture that ranges from a set of predefined parameters such as conversation flow and content and body animation of the agent, all the way to automatically generated assets such as voice and mouth animation.

This presentation will provide an architectural overview of the system for conversation generation.

Speaker: Vjekoslav Ranogajec, Director of Solutions and Custom Development

Tensor-based algorithms are increasingly finding significant applications in machine learning, particularly computer vision, somewhat because they offer a structure-exploiting approach. Many of those are based on tensor contractions and different tensor factorizations.

In this session, we will introduce the basic definitions and operations, present several factorizations with applications such as face recognition and shadow reduction, and set up a motivation for more advanced uses in the field of neural networks.

Speaker: Lana Periša, R&D Engineer

One of the challenges we encountered in developing a vision system was working with data collected in China. While the numerical results of development can be used without limitations, that is not the case with images and video. Constraints on usage include exporting overly-long footage, exporting footage containing important landmarks, or exporting footage at all.

This presentation describes some of those constraints and, where possible, the solutions that enabled us to deal with them.

Speakers: Vedran Pamuković, Senior R&D Engineer & Filip Soldan, Software Engineer in Test

During the Student Internship of 2020, six students from the University of Zagreb banded together with mentors from Visage Technologies to tackle five computer vision challenges in the automotive industry. They tackled Kalman Filters, Depth Estimation, Generative Adversarial Networks, Data Reduction, and Data Augmentation and had a great time in the process. Each of them successfully completed their challenge and presented it to the entire Visage Technologies team at the end. Find out more about their challenges and results in this presentation.

Speaker: Anamarija Čavka, Data and Machine Learning Specialist

Shopping is increasingly moving online, and 2020 certainly gave it an additional push. To help brands keep up with new habits and expectations of their customers, we developed makeup|SDK – the technology that lets users virtually try on makeup in real time, just like a mirror. This presentation will explain why we decided to create a new SDK, how we managed to develop this new product in only 3 months, and what we hope to achieve in the future.

Speaker: Lea Alfier, Software Engineer


Session time Session title
11.00 - 11.15
11.20 . 11.50
Inference engines and one API to rule them all
11.55 - 12.25
Regression-based methods for face alignment
12.25 - 12.35
Short break
12.35 - 13.05

(parallel sessions)
How we developed virtual try-on technology in only 3 months

Tensors in computer vision
13.05 - 13.35
Lunch break
13.35 - 14.00
Computer Vision Quiz
14.00 - 14.40

(parallel sessions)
Quality assurance infrastructure for face tracking algorithm

MarIA - virtual agent architecture focused on automatic generation of assets for speech
14.40 - 14.50
Short break
14.50 - 15.20

(parallel sessions)
Internship at Visage Technologies: The approach to solving computer vision challenges

Using data collected in China
15.20 - 15.30
Closing word

Thank you!

Thank you for your interest. The registrations are now closed, but make sure to follow us on Facebook, LinkedIn or Twitter to stay up-to-date with our future events.