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Hi there, I am Heitor Rapela!

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  • 🔭 I’m currently working on my Cross-Modal Detection
  • 🌱 I’m currently learning Pytorch Lightning
  • 📫 How to reach me: @heitorrapela on Twitter

Overview

Hi, I am Heitor Rapela, a Ph.D. student working with cross-modal detection (e.g., RGB <-> Infrared detection). My advisors are Marco Pedersoli and Eric Granger.

During my bachelor's in Computer Engineer, I cofounded the Ardurec Community and the RoboCIn. At this time, I was the computer vision manager of the RoboCIn Very Small Size team.

My undergrad project was about clustering with self-organizing maps using deep learning features. This work granted me a journal (Computer Vision and Image Understanding - CVIU) and helped me build confidence in my research.

In my undergrad and Master's, I was advised by professor Hansenclever Bassani, one of the best researchers that I know. I also worked on many side projects with professor Edna Barros, like detection and recognition projects in CETENE.

After that, I continued working with computer vision but started to learn and apply machine learning techniques in personal projects and academic projects.

I did my Master's degree in the field of unsupervised deep learning, which I developed with my coworker the deep clustering self-organizing maps with relevance learning. You can check in my google scholar papers. I also worked during my Master's with detection, semantic segmentation, and reinforcement learning.

In my spare time, I like to learn and compete @Kaggle.

Languages and Tools

GitHub Stats

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Kaggle Stats (Sum of different categories)

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Heitor Rapela Medeiros's Projects

.tmux icon .tmux

🇫🇷 Oh my tmux! My self-contained, pretty & versatile tmux configuration made with ❤️

ac-fpn icon ac-fpn

Implement of paper 《Attention-guided Context Feature Pyramid Network for Object Detection》

aligndet icon aligndet

Official code for ICCV 2023 Paper: AlignDet: Aligning Pre-training and Fine-tuning in Object Detection.

annotated_deep_learning_paper_implementations icon annotated_deep_learning_paper_implementations

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

anti-uav icon anti-uav

🔥🔥Official Repository for Anti-UAV🔥🔥

at-my-home icon at-my-home

Hello There, this is my rep to install the right way @home (ROS indigo + Gazebo)

awesome-rgbt-feature-fusion icon awesome-rgbt-feature-fusion

A collection of RGB-T-Feature-Fusion methods (deep learning methods mainly), codes, and datasets. The main directions involved are Multispectral Pedestrian, RGB-T Vehicle Detection, RGB-T Crowd Counting, RGB-T Fusion Tracking.

b-cos icon b-cos

B-cos Networks: Alignment is All we Need for Interpretability

baal icon baal

Library to enable Bayesian active learning in your research or labeling work.

bitcoinbook icon bitcoinbook

Mastering Bitcoin 2nd Edition - Programming the Open Blockchain

clean-fid icon clean-fid

PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

cleanrl icon cleanrl

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

coding icon coding

Repository created just to train :)

controlvideo icon controlvideo

Official pytorch implementation of "ControlVideo: Training-free Controllable Text-to-Video Generation"

convnetlayersstudy icon convnetlayersstudy

In this repository, I will put my studies in Convnet Layers following tutorials and implementing my undergraduate course project

crowd_counting_from_scratch icon crowd_counting_from_scratch

This is an overview and tutorial about crowd counting. In this repository, you can learn how to estimate number of pedestrians in crowd scenes through computer vision and deep learning.

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