PhD student at the UFRGS Computer Vision Lab being advised by Professor Claudio Jung. I'm interested in weakly supervised problems in machine learning and deep learning, especially applied to computer vision and object detection applications.
luisfelipezeni ▽ gmail.com - @luis_zeni - /github/ - /blog/
CVPR Deep Vision 2020
In this work, we claim that carefully selecting the aggregation criteria can considerably improve the accuracy of the learned detector. We start by proposing an additional refinement step to an existing approach (OICR), which we call refinement knowledge distillation. Then, we present an adaptive supervision aggregation function that dynamically changes the aggregation criteria for selecting boxes related to one of the ground-truth classes, background, or even ignored during the generation of each refinement module supervision. We call these improvements "Boosted-OICR".
SIBGRAPI 2018
We trained a real-time gender detector in the wild using DNN and visually analyzed which parts leads our model to make mistakes and the bias introduced by the training data.
EN: This "book" is a series of chapters to help students to learn to program in python (this content is in Portuguese language)
PT: Este livro contempla uma série de capítulos para ajudar estudantes a aprenderem a programar em python
I taught the following courses in the computer science department:
Web Development
Algorithms and Programming
Data Structures
Mobile Development (Android)
I taught the following courses in the technical informatics course:
Algorithms and Programming
Mobile Development (Android)
*Image Credits: Pixforce
Pixforce (Jan 2019 – Mar 2020)
At Pixforce I developed Computer Vision applications using Deep Learning models focused to the industry. I worked in four projects all focused in using computer vision to inspection propouses.
*Image Credits: Compuletra
Compuletra (Jan 2015 – Dec 2015)
I helped in the development of an Android App that uses computer vision to detect car plates and use this information to seek for stolen cars.