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| Author | Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno | ||||
| Title | Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era" | Type | Conference Article | ||
| Year | 2017 | Publication | Workshop on Big Data Analytics and Machine Learning for Data Communication Networks | Abbreviated Journal | |
| Volume | Issue | Pages | 43-48 | ||
| Keywords | mobile networks; call detail records; human mobility | ||||
| Abstract | The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes,
have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users. By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of “ always connected” terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users’ locations. |
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| Address | UCLA; USA; August 2017 | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-5054-9 | Medium | ||
| Area | Expedition | Conference  |
ACMW (SIGCOMM) | ||
| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FPT2017 | Serial | 2980 | ||
| Permanent link to this record | |||||
| Author | Aniol Lidon; Marc Bolaños; Mariella Dimiccoli; Petia Radeva; Maite Garolera; Xavier Giro | ||||
| Title | Semantic Summarization of Egocentric Photo-Stream Events | Type | Conference Article | ||
| Year | 2017 | Publication | 2nd Workshop on Lifelogging Tools and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Address | San Francisco; USA; October 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-5503-2 | Medium | ||
| Area | Expedition | Conference |
ACMW (LTA) | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ LBD2017 | Serial | 3024 | ||
| Permanent link to this record | |||||
| Author | Silvio Giancola; Anthony Cioppa; Adrien Deliege; Floriane Magera; Vladimir Somers; Le Kang; Xin Zhou; Olivier Barnich; Christophe De Vleeschouwer; Alexandre Alahi; Bernard Ghanem; Marc Van Droogenbroeck; Abdulrahman Darwish; Adrien Maglo; Albert Clapes; Andreas Luyts; Andrei Boiarov; Artur Xarles; Astrid Orcesi; Avijit Shah; Baoyu Fan; Bharath Comandur; Chen Chen; Chen Zhang; Chen Zhao; Chengzhi Lin; Cheuk-Yiu Chan; Chun Chuen Hui; Dengjie Li; Fan Yang; Fan Liang; Fang Da; Feng Yan; Fufu Yu; Guanshuo Wang; H. Anthony Chan; He Zhu; Hongwei Kan; Jiaming Chu; Jianming Hu; Jianyang Gu; Jin Chen; Joao V. B. Soares; Jonas Theiner; Jorge De Corte; Jose Henrique Brito; Jun Zhang; Junjie Li; Junwei Liang; Leqi Shen; Lin Ma; Lingchi Chen; Miguel Santos Marques; Mike Azatov; Nikita Kasatkin; Ning Wang; Qiong Jia; Quoc Cuong Pham; Ralph Ewerth; Ran Song; Rengang Li; Rikke Gade; Ruben Debien; Runze Zhang; Sangrok Lee; Sergio Escalera; Shan Jiang; Shigeyuki Odashima; Shimin Chen; Shoichi Masui; Shouhong Ding; Sin-wai Chan; Siyu Chen; Tallal El-Shabrawy; Tao He; Thomas B. Moeslund; Wan-Chi Siu; Wei Zhang; Wei Li; Xiangwei Wang; Xiao Tan; Xiaochuan Li; Xiaolin Wei; Xiaoqing Ye; Xing Liu; Xinying Wang; Yandong Guo; Yaqian Zhao; Yi Yu; Yingying Li; Yue He; Yujie Zhong; Zhenhua Guo; Zhiheng Li | ||||
| Title | SoccerNet 2022 Challenges Results | Type | Conference Article | ||
| Year | 2022 | Publication | 5th International ACM Workshop on Multimedia Content Analysis in Sports | Abbreviated Journal | |
| Volume | Issue | Pages | 75-86 | ||
| Keywords | |||||
| Abstract | The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on this https URL. Baselines and development kits are available on this https URL. | ||||
| Address | Lisboa; Portugal; October 2022 | ||||
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| Area | Expedition | Conference |
ACMW | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ GCD2022 | Serial | 3801 | ||
| Permanent link to this record | |||||
| Author | H. Emrah Tasli; Jan van Gemert; Theo Gevers | ||||
| Title | Spot the differences: from a photograph burst to the single best picture | Type | Conference Article | ||
| Year | 2013 | Publication | 21ST ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | 729-732 | ||
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| Abstract | With the rise of the digital camera, people nowadays typically take several near-identical photos of the same scene to maximize the chances of a good shot. This paper proposes a user-friendly tool for exploring a personal photo gallery for selecting or even creating the best shot of a scene between its multiple alternatives. This functionality is realized through a graphical user interface where the best viewpoint can be selected from a generated panorama of the scene. Once the viewpoint is selected, the user is able to go explore possible alternatives coming from the other images. Using this tool, one can explore a photo gallery efficiently. Moreover, additional compositions from other images are also possible. With such additional compositions, one can go from a burst of photographs to the single best one. Even funny compositions of images, where you can duplicate a person in the same image, are possible with our proposed tool. | ||||
| Address | Barcelona | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference |
ACM-MM | ||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | TGG2013 | Serial | 2368 | ||
| Permanent link to this record | |||||
| Author | Sezer Karaoglu; Jan van Gemert; Theo Gevers | ||||
| Title | Con-text: text detection using background connectivity for fine-grained object classification | Type | Conference Article | ||
| Year | 2013 | Publication | 21ST ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | 757-760 | ||
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| Area | Expedition | Conference |
ACM-MM | ||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ KGG2013 | Serial | 2369 | ||
| Permanent link to this record | |||||
| Author | Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz | ||||
| Title | SDIT: Scalable and Diverse Cross-domain Image Translation | Type | Conference Article | ||
| Year | 2019 | Publication | 27th ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | 1267–1276 | ||
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| Abstract | Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces. | ||||
| Address | Nice; Francia; October 2019 | ||||
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| Area | Expedition | Conference |
ACM-MM | ||
| Notes | LAMP; 600.106; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WGW2019 | Serial | 3363 | ||
| Permanent link to this record | |||||
| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Document noise removal using sparse representations over learned dictionary | Type | Conference Article | ||
| Year | 2013 | Publication | Symposium on Document engineering | Abbreviated Journal | |
| Volume | Issue | Pages | 161-168 | ||
| Keywords | |||||
| Abstract | best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental results on several datasets demonstrate the robustness of our method compared with the state-of-the-art. |
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| Address | Barcelona; October 2013 | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-1789-4 | Medium | ||
| Area | Expedition | Conference |
ACM-DocEng | ||
| Notes | DAG; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ DTR2013a | Serial | 2330 | ||
| Permanent link to this record | |||||
| Author | Marc Bolaños; Maite Garolera; Petia Radeva | ||||
| Title | Active labeling application applied to food-related object recognition | Type | Conference Article | ||
| Year | 2013 | Publication | 5th International Workshop on Multimedia for Cooking & Eating Activities | Abbreviated Journal | |
| Volume | Issue | Pages | 45-50 | ||
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| Abstract | Every day, lifelogging devices, available for recording different aspects of our daily life, increase in number, quality and functions, just like the multiple applications that we give to them. Applying wearable devices to analyse the nutritional habits of people is a challenging application based on acquiring and analyzing life records in long periods of time. However, to extract the information of interest related to the eating patterns of people, we need automatic methods to process large amount of life-logging data (e.g. recognition of food-related objects). Creating a rich set of manually labeled samples to train the algorithms is slow, tedious and subjective. To address this problem, we propose a novel method in the framework of Active Labeling for construct- ing a training set of thousands of images. Inspired by the hierarchical sampling method for active learning [6], we propose an Active forest that organizes hierarchically the data for easy and fast labeling. Moreover, introducing a classifier into the hierarchical structures, as well as transforming the feature space for better data clustering, additionally im- prove the algorithm. Our method is successfully tested to label 89.700 food-related objects and achieves significant reduction in expert time labelling.
Active labeling application applied to food-related object recognition ResearchGate. Available from: http://www.researchgate.net/publication/262252017Activelabelingapplicationappliedtofood-relatedobjectrecognition [accessed Jul 14, 2015]. |
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| Address | Barcelona; October 2013 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference |
ACM-CEA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGR2013b | Serial | 2637 | ||
| Permanent link to this record | |||||
| Author | Oriol Ramos Terrades; Alejandro Hector Toselli; Nicolas Serrano; Veronica Romero; Enrique Vidal; Alfons Juan | ||||
| Title | Interactive layout analysis and transcription systems for historic handwritten documents | Type | Conference Article | ||
| Year | 2010 | Publication | 10th ACM Symposium on Document Engineering | Abbreviated Journal | |
| Volume | Issue | Pages | 219–222 | ||
| Keywords | Handwriting recognition; Interactive predictive processing; Partial supervision; Interactive layout analysis | ||||
| Abstract | The amount of digitized legacy documents has been rising dramatically over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents, waiting to be classified and finally transcribed into a textual electronic format (such as ASCII or PDF). Nevertheless, most of the available fully-automatic applications addressing this task are far from being perfect and heavy and inefficient human intervention is often required to check and correct the results of such systems. In contrast, multimodal interactive-predictive approaches may allow the users to participate in the process helping the system to improve the overall performance. With this in mind, two sets of recent advances are introduced in this work: a novel interactive method for text block detection and two multimodal interactive handwritten text transcription systems which use active learning and interactive-predictive technologies in the recognition process. | ||||
| Address | Manchester, United Kingdom | ||||
| Corporate Author | Thesis | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference |
ACM | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @RTS2010 | Serial | 1857 | ||
| Permanent link to this record | |||||
| Author | Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen | ||||
| Title | Tex-Nets: Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition | Type | Conference Article | ||
| Year | 2017 | Publication | 19th International Conference on Multimodal Interaction | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Convolutional Neural Networks; Texture Recognition; Local Binary Paterns | ||||
| Abstract | Recognizing materials and textures in realistic imaging conditions is a challenging computer vision problem. For many years, local features based orderless representations were a dominant approach for texture recognition. Recently deep local features, extracted from the intermediate layers of a Convolutional Neural Network (CNN), are used as filter banks. These dense local descriptors from a deep model, when encoded with Fisher Vectors, have shown to provide excellent results for texture recognition. The CNN models, employed in such approaches, take RGB patches as input and train on a large amount of labeled images. We show that CNN models, which we call TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard deep models trained on RGB patches. We further investigate two deep architectures, namely early and late fusion, to combine the texture and color information. Experiments on benchmark texture datasets clearly demonstrate that TEX-Nets provide complementary information to standard RGB deep network. Our approach provides a large gain of 4.8%, 3.5%, 2.6% and 4.1% respectively in accuracy on the DTD, KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets, compared to the standard RGB network of the same architecture. Further, our final combination leads to consistent improvements over the state-of-the-art on all four datasets. | ||||
| Address | Glasgow; Scothland; November 2017 | ||||
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| Area | Expedition | Conference |
ACM | ||
| Notes | LAMP; 600.109; 600.068; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ RKW2017 | Serial | 3038 | ||
| Permanent link to this record | |||||
| Author | Xinhang Song; Haitao Zeng; Sixian Zhang; Luis Herranz; Shuqiang Jiang | ||||
| Title | Generalized Zero-shot Learning with Multi-source Semantic Embeddings for Scene Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | 28th ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Recognizing visual categories from semantic descriptions is a promising way to extend the capability of a visual classifier beyond the concepts represented in the training data (i.e. seen categories). This problem is addressed by (generalized) zero-shot learning methods (GZSL), which leverage semantic descriptions that connect them to seen categories (e.g. label embedding, attributes). Conventional GZSL are designed mostly for object recognition. In this paper we focus on zero-shot scene recognition, a more challenging setting with hundreds of categories where their differences can be subtle and often localized in certain objects or regions. Conventional GZSL representations are not rich enough to capture these local discriminative differences. Addressing these limitations, we propose a feature generation framework with two novel components: 1) multiple sources of semantic information (i.e. attributes, word embeddings and descriptions), 2) region descriptions that can enhance scene discrimination. To generate synthetic visual features we propose a two-step generative approach, where local descriptions are sampled and used as conditions to generate visual features. The generated features are then aggregated and used together with real features to train a joint classifier. In order to evaluate the proposed method, we introduce a new dataset for zero-shot scene recognition with multi-semantic annotations. Experimental results on the proposed dataset and SUN Attribute dataset illustrate the effectiveness of the proposed method. | ||||
| Address | Virtual; October 2020 | ||||
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| Publisher | Place of Publication | Editor | |||
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| Area | Expedition | Conference |
ACM | ||
| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ SZZ2020 | Serial | 3465 | ||
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| Author | Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe | ||||
| Title | Retrieval Guided Unsupervised Multi-domain Image to Image Translation | Type | Conference Article | ||
| Year | 2020 | Publication | 28th ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. | ||||
| Address | |||||
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| Area | Expedition | Conference |
ACM | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GLN2020 | Serial | 3497 | ||
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| Author | Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat | ||||
| Title | Nightime Vehicle Detecion for Intelligent Headlight Control | Type | Conference Article | ||
| Year | 2008 | Publication | Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, | Abbreviated Journal | |
| Volume | 5259 | Issue | Pages | 113–124 | |
| Keywords | Intelligent Headlights; vehicle detection | ||||
| Abstract | |||||
| Address | Juan-les-Pins, France | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference |
ACIVS | ||
| Notes | ADAS;CIC | Approved | no | ||
| Call Number | ADAS @ adas @ LHB2008a | Serial | 1098 | ||
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| Author | Mohammad Rouhani; Angel Sappa | ||||
| Title | A Novel Approach to Geometric Fitting of Implicit Quadrics | Type | Conference Article | ||
| Year | 2009 | Publication | 8th International Conference on Advanced Concepts for Intelligent Vision Systems | Abbreviated Journal | |
| Volume | 5807 | Issue | Pages | 121–132 | |
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| Abstract | This paper presents a novel approach for estimating the geometric distance from a given point to the corresponding implicit quadric curve/surface. The proposed estimation is based on the height of a tetrahedron, which is used as a coarse but reliable estimation of the real distance. The estimated distance is then used for finding the best set of quadric parameters, by means of the Levenberg-Marquardt algorithm, which is a common framework in other geometric fitting approaches. Comparisons of the proposed approach with previous ones are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. | ||||
| Address | Bordeaux, France | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-04696-4 | Medium | |
| Area | Expedition | Conference |
ACIVS | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ RoS2009 | Serial | 1194 | ||
| Permanent link to this record | |||||
| Author | Cesar Isaza; Joaquin Salas; Bogdan Raducanu | ||||
| Title | Toward the Detection of Urban Infrastructures Edge Shadows | Type | Conference Article | ||
| Year | 2010 | Publication | 12th International Conference on Advanced Concepts for Intelligent Vision Systems | Abbreviated Journal | |
| Volume | 6474 | Issue | I | Pages | 30–37 |
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| Abstract | In this paper, we propose a novel technique to detect the shadows cast by urban infrastructure, such as buildings, billboards, and traffic signs, using a sequence of images taken from a fixed camera. In our approach, we compute two different background models in parallel: one for the edges and one for the reflected light intensity. An algorithm is proposed to train the system to distinguish between moving edges in general and edges that belong to static objects, creating an edge background model. Then, during operation, a background intensity model allow us to separate between moving and static objects. Those edges included in the moving objects and those that belong to the edge background model are subtracted from the current image edges. The remaining edges are the ones cast by urban infrastructure. Our method is tested on a typical crossroad scene and the results show that the approach is sound and promising. | ||||
| Address | Sydney, Australia | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | eds. Blanc–Talon et al | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-17687-6 | Medium | |
| Area | Expedition | Conference |
ACIVS | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ ISR2010 | Serial | 1458 | ||
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