arXiv subjects

João Batista Neto

Publications and source records attributed to João Batista Neto.

3 recordsLinked to original sources

arXiv2026

Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification

Skin tone classification is a fundamental task in computer vision, with applications in quantifying medical features, assessing demographic representativeness, and mitigating algorithmic bias. However, existing datasets either rely on a restrictive 6-tone Fitzpatrick scale, prohibit training through restrictive licenses, or lack representation of extreme skin tones, while current methods often fail to disentangle true skin tone from in-the-wild imaging conditions, degrading to random-classifier performance. We introduce skin tone in the wild (STW), the first open-access dataset annotated with the 10-tone Monk Skin Tone (MST) scale, containing over 42K images and specifically curated to include underrepresented tones. Alongside a rigorous evaluation protocol, we propose SkinToneNet, a ViT-based model that leverages global image context for robust in-the-wild generalization. Experiments show that SkinToneNet achieves 90% weighted off-by-one accuracy, substantially outperforming prior baselines. We further use SkinToneNet to audit widely used face datasets, including CelebA and VGGFace2, revealing substantial representation imbalances across MST tones.

cs.CV↗

arXiv2024

Lessons from Building StackSpot AI: A Contextualized AI Coding Assistant

With their exceptional natural language processing capabilities, tools based on Large Language Models (LLMs) like ChatGPT and Co-Pilot have swiftly become indispensable resources in the software developer's toolkit. While recent studies suggest the potential productivity gains these tools can unlock, users still encounter drawbacks, such as generic or incorrect answers. Additionally, the pursuit of improved responses often leads to extensive prompt engineering efforts, diverting valuable time from writing code that delivers actual value. To address these challenges, a new breed of tools, built atop LLMs, is emerging. These tools aim to mitigate drawbacks by employing techniques like fine-tuning or enriching user prompts with contextualized information. In this paper, we delve into the lessons learned by a software development team venturing into the creation of such a contextualized LLM-based application, using retrieval-based techniques, called CodeBuddy. Over a four-month period, the team, despite lacking prior professional experience in LLM-based applications, built the product from scratch. Following the initial product release, we engaged with the development team responsible for the code generative components. Through interviews and analysis of the application's issue tracker, we uncover various intriguing challenges that teams working on LLM-based applications might encounter. For instance, we found three main group of lessons: LLM-based lessons, User-based lessons, and Technical lessons. By understanding these lessons, software development teams could become better prepared to build LLM-based applications.

cs.SE↗

arXiv2016

Segmentation of large images based on super-pixels and community detection in graphs

Image segmentation has many applications which range from machine learning to medical diagnosis. In this paper, we propose a framework for the segmentation of images based on super-pixels and algorithms for community identification in graphs. The super-pixel pre-segmentation step reduces the number of nodes in the graph, rendering the method the ability to process large images. Moreover, community detection algorithms provide more accurate segmentation than traditional approaches, such as those based on spectral graph partition. We also compare our method with two algorithms: a) the graph-based approach by Felzenszwalb and Huttenlocher and b) the contour-based method by Arbelaez. Results have shown that our method provides more precise segmentation and is faster than both of them.

cs.CV↗