1,720,962 research outputs found
Leveraging Synthetic Data for Zero–Shot and Few–Shot Circle Detection in Real–World Domains
Full text linkCircle detection plays a pivotal role in computer vision, underpinning applications from industrial inspection and bioinformatics to autonomous driving. Traditional methods, however, often struggle with real–world complexities, as they demand extensive parameter tuning and adaptation across different domains. In this paper, we present the Synthetic Circle Dataset (SynCircle), a large synthetic image dataset designed to train a YOLO v10 network for circle detection. The YOLO v10 network, pre–trained solely on synthetic data, demonstrates remarkable off–the–shelf performance that surpasses conventional methods in various practical scenarios. Furthermore, we show that incorporating just a few labeled real images for fine–tuning can significantly boost performance, reducing the need for large annotated datasets. To promote reproducibility and streamline adoption, we publicly release both the trained YOLO v10 weights and the full SynCircle dataset
Facial Segmentation in Deepfake Classification: a Transfer Learning Approach
Full text linkArtificial Intelligence (AI)–generated images represent a significant threat in various fields, such as security, privacy, media forensics and content moderation. In this paper, a novel approach for the detection of StyleGAN2–generated human faces is presented, leveraging a Transfer Learning strategy to improve the Classification performance of the models. A modified version of the state– of–the–art semantic segmentation model DeepLabV3+, using either a ResNet50 or a MobileNetV3 Large as feature extraction backbones, is used to create both a face segmentation model and the synthetic image detector. To achieve this goal, the models are at first trained for face segmentation in a multi–class Classification task on a widely used semantic segmentation dataset, achieving remarkable results for both configurations. Then, the pre–trained models are retrained on a collection of real and generated images, gathered from different sources to solve a binary Classification task, namely to detect synthetic (i.e. generated) images, thus carrying out two different transfer learning strategies. The results indicate that this targeted methodology significantly improves the detection rates compared to analyzing the face as a whole, and underlines the importance of advanced image recognition technologies when tackling the challenge of detecting generated faces
A Hybrid Deep Learning Approach for Liver Tumor Segmentation Using DeepLabV3+ and Hidden Markov Models
No full textThe liver, being the largest solid organ in the human body, is one of the most important players in metabolism and digestion processes. It is also a site where both primary and secondary tumors — originating from distant organs such as the lungs or other abdominal parts such as the pancreas and colon — can originate or metastasize. Therefore, the liver is regularly screened for the presence of lesions. These lesions require precise segmentation techniques to accurately diagnose cancer and improve patient monitoring, disease progression, and response to treatment. In this paper, a slightly modified version of a DeepLabV3+ network, a well–known and state–of–the–art segmentation model, paired with a Hidden Markov Model (HMM) based noise reduction module, is employed and trained on the Medical Segmentation Decathlon (MSD) liver tumor data set. This collection of liver lesions is a fraction of the MSD international challenge dedicated to identifying a general– purpose algorithm for medical image segmentation. The model is then evaluated on the test set of the same dataset with pixel–pixel accuracy and Intersection over Union (IoU)
Generated or Not Generated (GNG): The Importance of Background in the Detection of Fake Images
Full text linkFacial biometrics are widely used to reliably and conveniently recognize people in photos, in videos, or from real-time webcam streams. It is therefore of fundamental importance to detect synthetic faces in images in order to reduce the vulnerability of biometrics-based security systems. Furthermore, manipulated images of faces can be intentionally shared on social media to spread fake news related to the targeted individual. This paper shows how fake face recognition models may mainly rely on the information contained in the background when dealing with generated faces, thus reducing their effectiveness. Specifically, a classifier is trained to separate fake images from real ones, using their representation in a latent space. Subsequently, the faces are segmented and the background removed, and the detection procedure is performed again, observing a significant drop in classification accuracy. Finally, an explainability tool (SHAP) is used to highlight the salient areas of the image, showing that the background and face contours crucially influence the classifier decision
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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