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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.20142">arXiv:2409.20142</a> <span> [<a href="https://arxiv.org/pdf/2409.20142">pdf</a>, <a href="https://arxiv.org/format/2409.20142">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Signal Processing for Haptic Surface Modeling: a Review </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Stefani%2C+A+L">Antonio Luigi Stefani</a>, <a href="/search/cs?searchtype=author&query=Bisagno%2C+N">Niccol貌 Bisagno</a>, <a href="/search/cs?searchtype=author&query=Rosani%2C+A">Andrea Rosani</a>, <a href="/search/cs?searchtype=author&query=Conci%2C+N">Nicola Conci</a>, <a href="/search/cs?searchtype=author&query=De+Natale%2C+F">Francesco De Natale</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.20142v1-abstract-short" style="display: inline;"> Haptic feedback has been integrated into Virtual and Augmented Reality, complementing acoustic and visual information and contributing to an all-round immersive experience in multiple fields, spanning from the medical domain to entertainment and gaming. Haptic technologies involve complex cross-disciplinary research that encompasses sensing, data representation, interactive rendering, perception,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.20142v1-abstract-full').style.display = 'inline'; document.getElementById('2409.20142v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.20142v1-abstract-full" style="display: none;"> Haptic feedback has been integrated into Virtual and Augmented Reality, complementing acoustic and visual information and contributing to an all-round immersive experience in multiple fields, spanning from the medical domain to entertainment and gaming. Haptic technologies involve complex cross-disciplinary research that encompasses sensing, data representation, interactive rendering, perception, and quality of experience. The standard processing pipeline, consists of (I) sensing physical features in the real world using a transducer, (II) modeling and storing the collected information in some digital format, (III) communicating the information, and finally, (IV) rendering the haptic information through appropriate devices, thus producing a user experience (V) perceptually close to the original physical world. Among these areas, sensing, rendering and perception have been deeply investigated and are the subject of different comprehensive surveys available in the literature. Differently, research dealing with haptic surface modeling and data representation still lacks a comprehensive dissection. In this work, we aim at providing an overview on modeling and representation of haptic surfaces from a signal processing perspective, covering the aspects that lie in between haptic information acquisition on one side and rendering and perception on the other side. We analyze, categorize, and compare research papers that address the haptic surface modeling and data representation, pointing out existing gaps and possible research directions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.20142v1-abstract-full').style.display = 'none'; document.getElementById('2409.20142v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.00388">arXiv:2408.00388</a> <span> [<a href="https://arxiv.org/pdf/2408.00388">pdf</a>, <a href="https://arxiv.org/ps/2408.00388">ps</a>, <a href="https://arxiv.org/format/2408.00388">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Deepfake Media Forensics: State of the Art and Challenges Ahead </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Amerini%2C+I">Irene Amerini</a>, <a href="/search/cs?searchtype=author&query=Barni%2C+M">Mauro Barni</a>, <a href="/search/cs?searchtype=author&query=Battiato%2C+S">Sebastiano Battiato</a>, <a href="/search/cs?searchtype=author&query=Bestagini%2C+P">Paolo Bestagini</a>, <a href="/search/cs?searchtype=author&query=Boato%2C+G">Giulia Boato</a>, <a href="/search/cs?searchtype=author&query=Bonaventura%2C+T+S">Tania Sari Bonaventura</a>, <a href="/search/cs?searchtype=author&query=Bruni%2C+V">Vittoria Bruni</a>, <a href="/search/cs?searchtype=author&query=Caldelli%2C+R">Roberto Caldelli</a>, <a href="/search/cs?searchtype=author&query=De+Natale%2C+F">Francesco De Natale</a>, <a href="/search/cs?searchtype=author&query=De+Nicola%2C+R">Rocco De Nicola</a>, <a href="/search/cs?searchtype=author&query=Guarnera%2C+L">Luca Guarnera</a>, <a href="/search/cs?searchtype=author&query=Mandelli%2C+S">Sara Mandelli</a>, <a href="/search/cs?searchtype=author&query=Marcialis%2C+G+L">Gian Luca Marcialis</a>, <a href="/search/cs?searchtype=author&query=Micheletto%2C+M">Marco Micheletto</a>, <a href="/search/cs?searchtype=author&query=Montibeller%2C+A">Andrea Montibeller</a>, <a href="/search/cs?searchtype=author&query=Orru%27%2C+G">Giulia Orru'</a>, <a href="/search/cs?searchtype=author&query=Ortis%2C+A">Alessandro Ortis</a>, <a href="/search/cs?searchtype=author&query=Perazzo%2C+P">Pericle Perazzo</a>, <a href="/search/cs?searchtype=author&query=Puglisi%2C+G">Giovanni Puglisi</a>, <a href="/search/cs?searchtype=author&query=Salvi%2C+D">Davide Salvi</a>, <a href="/search/cs?searchtype=author&query=Tubaro%2C+S">Stefano Tubaro</a>, <a href="/search/cs?searchtype=author&query=Tonti%2C+C+M">Claudia Melis Tonti</a>, <a href="/search/cs?searchtype=author&query=Villari%2C+M">Massimo Villari</a>, <a href="/search/cs?searchtype=author&query=Vitulano%2C+D">Domenico Vitulano</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.00388v2-abstract-short" style="display: inline;"> AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfakes, producing highly realistic yet fabricated content. While these technologies open up new creative possibilities, they also bring substantial ethical… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.00388v2-abstract-full').style.display = 'inline'; document.getElementById('2408.00388v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.00388v2-abstract-full" style="display: none;"> AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfakes, producing highly realistic yet fabricated content. While these technologies open up new creative possibilities, they also bring substantial ethical and security risks due to their potential misuse. The rise of such advanced media has led to the development of a cognitive bias known as Impostor Bias, where individuals doubt the authenticity of multimedia due to the awareness of AI's capabilities. As a result, Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques, especially Convolutional Neural Networks (CNNs). Research in forensic Deepfake technology encompasses five main areas: detection, attribution and recognition, passive authentication, detection in realistic scenarios, and active authentication. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.00388v2-abstract-full').style.display = 'none'; document.getElementById('2408.00388v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.15481">arXiv:2111.15481</a> <span> [<a href="https://arxiv.org/pdf/2111.15481">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3390/drones5040127">10.3390/drones5040127 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Raza%2C+W">Wamiq Raza</a>, <a href="/search/cs?searchtype=author&query=Osman%2C+A">Anas Osman</a>, <a href="/search/cs?searchtype=author&query=Ferrini%2C+F">Francesco Ferrini</a>, <a href="/search/cs?searchtype=author&query=De+Natale%2C+F">Francesco De Natale</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2111.15481v1-abstract-short" style="display: inline;"> In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and completion of energy-demanding tasks. The possibility of providing UAVs with advanced decision-making capab… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.15481v1-abstract-full').style.display = 'inline'; document.getElementById('2111.15481v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.15481v1-abstract-full" style="display: none;"> In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and completion of energy-demanding tasks. The possibility of providing UAVs with advanced decision-making capabilities in an energy-effective way would be extremely beneficial. In this paper, we propose a practical solution to this problem that exploits deep learning on the edge. The developed system integrates an OpenMV microcontroller into a DJI Tello Micro Aerial Vehicle (MAV). The microcontroller hosts a set of machine learning-enabled inference tools that cooperate to control the navigation of the drone and complete a given mission objective. The goal of this approach is to leverage the new opportunistic features of TinyML through OpenMV including offline inference, low latency, energy efficiency, and data security. The approach is successfully validated on a practical application consisting of the onboard detection of people wearing protection masks in a crowded environment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.15481v1-abstract-full').style.display = 'none'; document.getElementById('2111.15481v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.02515">arXiv:2108.02515</a> <span> [<a href="https://arxiv.org/pdf/2108.02515">pdf</a>, <a href="https://arxiv.org/format/2108.02515">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Multi-clue reconstruction of sharing chains for social media images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Verde%2C+S">Sebastiano Verde</a>, <a href="/search/cs?searchtype=author&query=Pasquini%2C+C">Cecilia Pasquini</a>, <a href="/search/cs?searchtype=author&query=Lago%2C+F">Federica Lago</a>, <a href="/search/cs?searchtype=author&query=Goller%2C+A">Alessandro Goller</a>, <a href="/search/cs?searchtype=author&query=De+Natale%2C+F+G">Francesco GB De Natale</a>, <a href="/search/cs?searchtype=author&query=Piva%2C+A">Alessandro Piva</a>, <a href="/search/cs?searchtype=author&query=Boato%2C+G">Giulia Boato</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.02515v1-abstract-short" style="display: inline;"> The amount of multimedia content shared everyday, combined with the level of realism reached by recent fake-generating technologies, threatens to impair the trustworthiness of online information sources. The process of uploading and sharing data tends to hinder standard media forensic analyses, since multiple re-sharing steps progressively hide the traces of past manipulations. At the same time th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.02515v1-abstract-full').style.display = 'inline'; document.getElementById('2108.02515v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.02515v1-abstract-full" style="display: none;"> The amount of multimedia content shared everyday, combined with the level of realism reached by recent fake-generating technologies, threatens to impair the trustworthiness of online information sources. The process of uploading and sharing data tends to hinder standard media forensic analyses, since multiple re-sharing steps progressively hide the traces of past manipulations. At the same time though, new traces are introduced by the platforms themselves, enabling the reconstruction of the sharing history of digital objects, with possible applications in information flow monitoring and source identification. In this work, we propose a supervised framework for the reconstruction of image sharing chains on social media platforms. The system is structured as a cascade of backtracking blocks, each of them tracing back one step of the sharing chain at a time. Blocks are designed as ensembles of classifiers trained to analyse the input image independently from one another by leveraging different feature representations that describe both content and container of the media object. Individual decisions are then properly combined by a late fusion strategy. Results highlight the advantages of employing multiple clues, which allow accurately tracing back up to three steps along the sharing chain. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.02515v1-abstract-full').style.display = 'none'; document.getElementById('2108.02515v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.07945">arXiv:1810.07945</a> <span> [<a href="https://arxiv.org/pdf/1810.07945">pdf</a>, <a href="https://arxiv.org/format/1810.07945">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Accurate and Scalable Image Clustering Based On Sparse Representation of Camera Fingerprint </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Phan%2C+Q">Quoc-Tin Phan</a>, <a href="/search/cs?searchtype=author&query=Boato%2C+G">Giulia Boato</a>, <a href="/search/cs?searchtype=author&query=De+Natale%2C+F+G+B">Francesco G. B. De Natale</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1810.07945v2-abstract-short" style="display: inline;"> Clustering images according to their acquisition devices is a well-known problem in multimedia forensics, which is typically faced by means of camera Sensor Pattern Noise (SPN). Such an issue is challenging since SPN is a noise-like signal, hard to be estimated and easy to be attenuated or destroyed by many factors. Moreover, the high dimensionality of SPN hinders large-scale applications. Existin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.07945v2-abstract-full').style.display = 'inline'; document.getElementById('1810.07945v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.07945v2-abstract-full" style="display: none;"> Clustering images according to their acquisition devices is a well-known problem in multimedia forensics, which is typically faced by means of camera Sensor Pattern Noise (SPN). Such an issue is challenging since SPN is a noise-like signal, hard to be estimated and easy to be attenuated or destroyed by many factors. Moreover, the high dimensionality of SPN hinders large-scale applications. Existing approaches are typically based on the correlation among SPNs in the pixel domain, which might not be able to capture intrinsic data structure in union of vector subspaces. In this paper, we propose an accurate clustering framework, which exploits linear dependencies among SPNs in their intrinsic vector subspaces. Such dependencies are encoded under sparse representations which are obtained by solving a LASSO problem with non-negativity constraint. The proposed framework is highly accurate in number of clusters estimation and image association. Moreover, our framework is scalable to the number of images and robust against double JPEG compression as well as the presence of outliers, owning big potential for real-world applications. Experimental results on Dresden and Vision database show that our proposed framework can adapt well to both medium-scale and large-scale contexts, and outperforms state-of-the-art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.07945v2-abstract-full').style.display = 'none'; document.getElementById('1810.07945v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1710.01013">arXiv:1710.01013</a> <span> [<a href="https://arxiv.org/pdf/1710.01013">pdf</a>, <a href="https://arxiv.org/format/1710.01013">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Training Feedforward Neural Networks with Standard Logistic Activations is Feasible </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sansone%2C+E">Emanuele Sansone</a>, <a href="/search/cs?searchtype=author&query=De+Natale%2C+F+G+B">Francesco G. B. De Natale</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1710.01013v1-abstract-short" style="display: inline;"> Training feedforward neural networks with standard logistic activations is considered difficult because of the intrinsic properties of these sigmoidal functions. This work aims at showing that these networks can be trained to achieve generalization performance comparable to those based on hyperbolic tangent activations. The solution consists on applying a set of conditions in parameter initializat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.01013v1-abstract-full').style.display = 'inline'; document.getElementById('1710.01013v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1710.01013v1-abstract-full" style="display: none;"> Training feedforward neural networks with standard logistic activations is considered difficult because of the intrinsic properties of these sigmoidal functions. This work aims at showing that these networks can be trained to achieve generalization performance comparable to those based on hyperbolic tangent activations. The solution consists on applying a set of conditions in parameter initialization, which have been derived from the study of the properties of a single neuron from an information-theoretic perspective. The proposed initialization is validated through an extensive experimental analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.01013v1-abstract-full').style.display = 'none'; document.getElementById('1710.01013v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 October, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1608.06807">arXiv:1608.06807</a> <span> [<a href="https://arxiv.org/pdf/1608.06807">pdf</a>, <a href="https://arxiv.org/ps/1608.06807">ps</a>, <a href="https://arxiv.org/format/1608.06807">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TPAMI.2018.2860995">10.1109/TPAMI.2018.2860995 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Efficient Training for Positive Unlabeled Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sansone%2C+E">Emanuele Sansone</a>, <a href="/search/cs?searchtype=author&query=De+Natale%2C+F+G+B">Francesco G. B. De Natale</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Z">Zhi-Hua Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1608.06807v4-abstract-short" style="display: inline;"> Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1608.06807v4-abstract-full').style.display = 'inline'; document.getElementById('1608.06807v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1608.06807v4-abstract-full" style="display: none;"> Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1608.06807v4-abstract-full').style.display = 'none'; document.getElementById('1608.06807v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 August, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to IEEE TPAMI</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 31 July 2018 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1608.06770">arXiv:1608.06770</a> <span> [<a href="https://arxiv.org/pdf/1608.06770">pdf</a>, <a href="https://arxiv.org/format/1608.06770">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Automatic Synchronization of Multi-User Photo Galleries </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sansone%2C+E">E. Sansone</a>, <a href="/search/cs?searchtype=author&query=Apostolidis%2C+K">K. Apostolidis</a>, <a href="/search/cs?searchtype=author&query=Conci%2C+N">N. Conci</a>, <a href="/search/cs?searchtype=author&query=Boato%2C+G">G. Boato</a>, <a href="/search/cs?searchtype=author&query=Mezaris%2C+V">V. Mezaris</a>, <a href="/search/cs?searchtype=author&query=De+Natale%2C+F+G+B">F. G. B. De Natale</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1608.06770v2-abstract-short" style="display: inline;"> In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solutio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1608.06770v2-abstract-full').style.display = 'inline'; document.getElementById('1608.06770v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1608.06770v2-abstract-full" style="display: none;"> In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solution that achieves better generalization performance for the synchronization task compared to the available literature. The method is characterized by three stages: at first, deep convolutional neural network features are used to assess the visual similarity among the photos; then, pairs of similar photos are detected across different galleries and used to construct a graph; eventually, a probabilistic graphical model is used to estimate the temporal offset of each pair of galleries, by traversing the minimum spanning tree extracted from this graph. The experimental evaluation is conducted on four publicly available datasets covering different types of events, demonstrating the strength of our proposed method. A thorough discussion of the obtained results is provided for a critical assessment of the quality in synchronization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1608.06770v2-abstract-full').style.display = 'none'; document.getElementById('1608.06770v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 August, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ACCEPTED to IEEE Transactions on Multimedia</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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