Paper-Conference

Pixel-Level Quality Indicator for Image Data Annotation
The creation of image classification and segmentation Machine Learning products requires the annotation of different classes by experts …
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Analysis of Point Cloud Domain Gap Effects for 3D Object Detection Evaluation
The development of autonomous driving systems heavily relies on high-quality LiDAR data, which is essential for robust object detection …
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Data and Label Graph Fusion for Semi-supervised Learning: Application to Image Categorization
In this paper, a novel framework for semi-supervised learning based on graphs is introduced. We present an innovative approach for …
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Large Scale Graph Construction and Label Propagation
Despite the advances in semi-supervised learning methods, these algorithms face three limitations. The first is the assumption of …
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Learning Gaze-aware Compositional GAN from Limited Annotations
Gaze-annotated facial data is crucial for training deep neural networks (DNNs) for gaze estimation. However, obtaining these data is …
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Multi-Level XAI-Driven MLOps Pipeline for the Adjustment of Fruit and Vegetable Classifiers
In this paper, we present a machine learning operations (MLOps) pipeline that exploits explainable artificial intelligence (XAI) to …
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Predicting Lung Infection Severity in Chest X-Ray Images Through Multi-score Assessment
Respiratory illnesses, such as COVID-19, pose a significant health challenge. The prompt and precise identification of pulmonary …
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Revolutionizing Skin Cancer Diagnosis: Unleashing AI Precision Through Deep Learning
Accurate detection methods are desperately needed, as skin cancer concerns around the world continue to rise. Conventional methods, …
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Self-supervised Vision Transformers for image-to-image labeling: a BiaPy solution to the LightMyCells Challenge
Fluorescence microscopy plays a crucial role in cellular analysis but is often hindered by phototoxicity and limited spectral channels. …
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Unsupervised data labeling and incremental cross-domain training for enhanced hybrid eye gaze estimation
This paper aims to advance the fields of unsupervised data labeling and incremental cross-domain training techniques. We apply these …
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Unsupervised data labeling and incremental cross-domain training for enhanced hybrid eye gaze estimation