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portfolio

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projects

Translating Tweets from Trumpese to Sanderese with Transformers and CycleGANs (2020)

Used Transformers to apply Cyclic Generative Adversarial Networks to the Natural Language Processing domain, attempting to transfer styles between tweets of different users

[Linguistics] Non-adult Behavior of Children’s Quantification in Logical Deduction Outside of the Language Domain (2020)

Proposed a psycholinguistic experiment to evaluate whether exhaustive pairing, a non-adult judgement common in children in ages 4 to 6, is caused by pragmatic or semantic reasons. Experimenters would induce children to make the deductive reasoning required by quantifiers like every without using them, and evaluate whether exhaustive pairing would persist.

Bayesian Few Shot Learning of Compositional Instructions (2019)

Developed a Bayesian Model that reproduced human behavior when given the sequence-to-sequence task of interpreting a list of instructions in an artificially generated language to generate a sequence of colors.

Meta-Visualization: Investigating Rapid Learning and Feature Reuse (2019)

Investigated the nature of the meta-learning process in algorithms like MAML through the development of a visualization tool for the learning path in the loss landscape and geometric interpretations of rapid learning and feature reuse.

Implementing a Fusion Tree in C++ (2017)

Implemented a fully functional and well documented fusion tree that can perform predecessor queries with a constant number of operations in a general BigInt.

CodCad (2016)

CodCad was an online platform created to teach competitive programming for free. I co-founded CodCad in 2016.

Noic (2016)

Noic is a project that promotes scientific olympiads in Brazil and democratizes access to them. I presided Noic in 2016

publications

Method and System for an End-to-End Deep Learning Based Optical Coherence Tomography (OCT) Multi Retinal Layer Segmentation

Published in US Patent, 2023

We use a Transformer-based model to segment retinal layers from OCT scans. We process an image as 1D sequence of A-scans and treat each of them as a token, instead of processing a 2D image, which is more computationally efficient.

Text-image Alignment for Diffusion-based Perception

Published in CVPR, 2024

We use automatically generated captions to improve the text-image alignment of a diffusion backbone in downstream visual tasks such as semantic segmentation, depth estimation and object detection. Our method also achieves improves the SOTA in both single-domain and cross-domain tasks.

Recommended citation: Neehar Kondapaneni, Markus Marks, Manuel Knott, Rogerio Guimaraes, Pietro Perona; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 13883-13893 https://arxiv.org/abs/2310.00031

Diffusion-Based Action Recognition Generalizes to Untrained Domains

Published in arXiv preprint, 2025

We propose using features generated by a Vision Diffusion Model (VDM), aggregated via a transformer, to achieve human-like action recognition across domain shifts. We find that generalization is enhanced by the use of a model conditioned on earlier timesteps of the diffusion process to highlight semantic information over pixel level details in the extracted features. Our model sets a new state-of-the-art across three generalization benchmarks, bringing machine action recognition closer to human-like robustness.

Recommended citation: Rogerio Guimaraes, Frank Xiao, Pietro Perona & Markus Marks. (2025). Diffusion-Based Action Recognition Generalizes to Untrained Domains. https://arxiv.org/abs/2509.08908

teaching

Informatics Olympiads Teacher

Middle and High School classes, Farias Brito, 2017

I was a teacher of competitive programming for middle and high school students preparing for the Brazilian Olympiad in Informatics and the International Olympiad in Informatics at Organização Educacional Farias Brito.

EE/CS 148: Large Language and Vision Models

Teaching Assistant, Caltech, 2023

I was a TA for EE/CS 148: Large Language and Vision Models at Caltech. We taught how large generative models, such as ChatGPT and Dall-E, process and produce realistic text and images.

Rogério Guimarães