arXiv subjects

Gabriel Fonseca Silva

Publications and source records attributed to Gabriel Fonseca Silva.

5 recordsLinked to original sources

arXiv2024

Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques

Machine learning has significantly advanced healthcare by aiding in disease prevention and treatment identification. However, accessing patient data can be challenging due to privacy concerns and strict regulations. Generating synthetic, realistic data offers a potential solution for overcoming these limitations, and recent studies suggest that fine-tuning foundation models can produce such data effectively. In this study, we explore the potential of foundation models for generating realistic medical images, particularly chest x-rays, and assess how their performance improves with fine-tuning. We propose using a Latent Diffusion Model, starting with a pre-trained foundation model and refining it through various configurations. Additionally, we performed experiments with input from a medical professional to assess the realism of the images produced by each trained model.

eess.IV↗

arXiv2024

Evaluating and Comparing Crowd Simulations: Perspectives from a Crowd Authoring Tool

Crowd simulation is a research area widely used in diverse fields, including gaming and security, assessing virtual agent movements through metrics like time to reach their goals, speed, trajectories, and densities. This is relevant for security applications, for instance, as different crowd configurations can determine the time people spend in environments trying to evacuate them. In this work, we extend WebCrowds, an authoring tool for crowd simulation, to allow users to build scenarios and evaluate them through a set of metrics. The aim is to provide a quantitative metric that can, based on simulation data, select the best crowd configuration in a certain environment. We conduct experiments to validate our proposed metric in multiple crowd simulation scenarios and perform a comparison with another metric found in the literature. The results show that experts in the domain of crowd scenarios agree with our proposed quantitative metric.

cs.GR↗

arXiv2023

Exploring Crowd Dynamics: Simulating Structured Behaviors through Crowd Simulation Models

This paper proposes the simulation of structured behaviors in a crowd of virtual agents by extending the BioCrowds simulation model. Three behaviors were simulated and evaluated, a queue as a generic case and two specific behaviors observed at rock concerts. The extended model incorporates new parameters and modifications to replicate these behaviors accurately. Experiments were conducted to analyze the impact of parameters on simulation results, and computational performance was considered. The results demonstrate the model's effectiveness in simulating structured behaviors and its potential for replicating complex social phenomena in diverse scenarios.

cs.GR↗

arXiv2022

LODUS: A Multi-Level Framework for Simulating Environment and Population -- A Contagion Experiment on a Pandemic World

Nowadays we are experiencing a way of life that never existed before. The pandemic has sharply changed our habits, customs, and behavior. In addition, a lot of work was suddenly requested for city managers challenging them to develop strategies to try stopping the pandemic progression. Urban environments must be dynamic and managers need fast decisions when working on crisis situations. In this paper we present LODUS, a framework able to simulate urban environments on a multi-level approach, combining macro and micro simulation information in order to provide accurate information about population dynamics. Furthermore, the framework LODUS is a powerful tool when performing an urban viability study, since the simulation results are able to highlight and predict attention points prior to an urban environment to be built.

cs.MA↗

arXiv2022

Perception of Personality Traits in Crowds of Virtual Humans

This paper proposes a perceptual visual analysis regarding the personality of virtual humans. Many studies have presented findings regarding the way human beings perceive virtual humans with respect to their faces, body animation, motion in the virtual environment and etc. We are interested in investigating the way people perceive visual manifestations of virtual humans' personality traits when they are interactive and organized in groups. Many applications in games and movies can benefit from the findings regarding the perceptual analysis with the main goal to provide more realistic characters and improve the users' experience. We provide experiments with subjects and obtained results indicate that, although is very subtle, people perceive more the extraversion (the personality trait that we measured), into the crowds of virtual humans, when interacting with virtual humans behaviors, than when just observing as a spectator camera.

cs.HC↗