Cauã M. Pereira
I research computer vision & deep learning.
Computer Science undergraduate at UFMG and previous visiting researcher at the University of Alberta, working on visual representation, image matching, and multimodal perception.
Hi, I'm
About Me
I’m a Computer Science undergraduate at the Federal University of Minas Gerais (UFMG) and a visiting researcher at the University of Alberta’s Vision and Learning Lab, selected through the competitive University of Alberta Research Experience 2026.
My research focuses on computer vision and deep learning — in particular visual representation, image matching, and multimodal perception. I’ve contributed to work under review at NeurIPS 2026 and built large-scale training pipelines on HPC clusters. I enjoy taking research ideas from the whiteboard all the way to robust, reproducible systems.
Technologies and tools I work with regularly:- Python
- C++
- PyTorch
- OpenCV
- NumPy / Pandas
- Docker
- Linux
- Slurm / HPC
- Weights & Biases
- Git
Experience
Student Researcher — Image Matching
-
VeRLab
Sep 2025 - Present
- Contributed to the ideation and experimental design of a novel keypoint descriptor for image matching, currently under review at NeurIPS 2026.
- Led benchmarking across five established benchmarks; the proposed method outperformed strong baselines on the majority while staying competitive on the rest.
- Extending the direction with reinforcement learning and reference-conditioning for keypoint detection, plus non-rigid synthetic data generation for training.
Student Researcher — Petrobras Multivisão 3 (3D Semantic Segmentation)
Aug 2025 - Present
- Adapted state-of-the-art point cloud models for complex real-world industrial environments.
- Built scalable pipelines for large-scale point cloud partitioning, dataset preparation, and performance optimization.
- Optimized model architectures to improve segmentation accuracy and robustness under challenging 3D conditions.
- Achieved state-of-the-art recognition (under 5% WER) on the recognition component of a multimodal, bidirectional sign language–speech translation system bridging communication between Deaf patients and healthcare professionals.
- Implemented a complete data pipeline (preprocessing + training acceleration), reducing training time by over 80% and improving generalization across users.
- Expanded and maintained a custom Libras dataset by coordinating volunteers and conducting new video recordings.
- Designed and optimized deep learning architectures (ViTs, CNNs, RNNs) with targeted data augmentation, and mentored a new lab member.
Visiting Researcher — Computer Vision
-
Vision and Learning Lab — University of Alberta
Jan 2026 - Apr 2026
Selected for the University of Alberta Research Experience 2026, a competitive international research program.
- Researched multimodal semantic segmentation, adapting deep learning models and data pipelines to fuse RGB and thermal (multispectral) imagery.
- Reviewed the state of the art in domain adaptation and its application to multispectral semantic segmentation.
- Built reproducible large-scale training pipelines on the Alliance of Canada’s HPC clusters using Slurm for GPU job scheduling and orchestration.
Student Researcher — IoT and Embedded Systems
-
IHM Stefanini — LITE
May 2023 - Apr 2024
- Developed a module that collects data from electrical and magnetic sensors and transmits it in an unstructured format over LoRa, using a custom multi-point-to-point protocol based on CSMA/CA, delivering data to an Azure container for further analysis.
- Worked at LITE (Laboratory of Informatics, Telecommunications, and Electronics of UFMG) as a Technological Initiation and Innovation scholarship holder on the Smart Industrial Pumps Monitoring (MoBI IoT) project, collaborating with the Engineering Director at IHM.
- Contributed to SADUMBA and FIFO2 — both master’s projects — enhancing IoT data transmission for dam monitoring and biomedical applications, respectively.
Electronics Intern — IoT
-
LITE
Jun 2022 - Dec 2022
- Built from scratch a system for the Smart Maturation Chamber of Minas Artisanal Cheese project (CMI-QMA IoT) — part of a Doctoral Technological Innovation initiative — implementing real-time environmental data acquisition and encrypted transmission.
- Assisted students with IoT projects for the microcontrollers discipline.
Education
2020 - 2022
Electronics Technician (Integrated High School)
COLTEC — UFMG
Integrated High School and Electronics Technician program at the Technical
College of the Federal University of Minas Gerais.
Publications
INTERACT 2025
Captar-Libras: A Bidirectional Translator with a Photorealistic Avatar
Santos, N. S. et al. (incl. C. M. Pereira). Medical pre-consultation translator for Deaf patients. INTERACT 2025.
Software Registrations
MoBI IoT Project — 7 modules (INPI/UFMG, 2024)
Industrial pump monitoring via LoRa, MQTT, and cloud infrastructure. Da Cunha, A.B., Reis, M.S., Pereira, C.M. et al.
SADUMBA Project — 7 modules (INPI/UFMG, 2023)
Dam monitoring via ultrasonic and vibrating wire sensors. Da Cunha, A.B., Bastos, W.S., Pereira, C.M. et al.
FIFO2 Project — 2 modules (INPI/UFMG, 2023)
Biomedical flowmeter control and visualization. Da Cunha, A.B., Santos, M.J.C., Pereira, C.M. et al.
Get in Touch
My inbox is always open.