|
Records |
Links ![sorted by URL field, descending order (down)](img/sort_desc.gif) |
|
Author |
Javier Rodenas; Bhalaji Nagarajan; Marc Bolaños; Petia Radeva |
![goto web page url](img/www.gif)
|
|
Title |
Learning Multi-Subset of Classes for Fine-Grained Food Recognition |
Type |
Conference Article |
|
Year |
2022 |
Publication |
7th International Workshop on Multimedia Assisted Dietary Management |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
17–26 |
|
|
Keywords |
|
|
|
Abstract |
Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets. |
|
|
Address |
Lisboa; Portugal; October 2022 |
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
MADiMa |
|
|
Notes |
MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ RNB2022 |
Serial |
3797 |
|
Permanent link to this record |
|
|
|
|
Author |
Xinhang Song; Haitao Zeng; Sixian Zhang; Luis Herranz; Shuqiang Jiang |
![goto web page url](img/www.gif)
|
|
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 |
|
|
|
Keywords |
|
|
|
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 |
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ACM |
|
|
Notes |
LAMP; 600.141; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SZZ2020 |
Serial |
3465 |
|
Permanent link to this record |
|
|
|
|
Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Self-Supervised Visual Representations for Cross-Modal Retrieval |
Type |
Conference Article |
|
Year |
2019 |
Publication |
ACM International Conference on Multimedia Retrieval |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
182–186 |
|
|
Keywords |
|
|
|
Abstract |
Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset. |
|
|
Address |
Otawa; Canada; june 2019 |
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICMR |
|
|
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ PGR2019 |
Serial |
3288 |
|
Permanent link to this record |
|
|
|
|
Author |
Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados |
![goto web page url](img/www.gif)
|
|
Title |
Classification of aesthetic natural scene images using statistical and semantic features |
Type |
Journal Article |
|
Year |
2023 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
|
Volume |
82 |
Issue |
9 |
Pages |
13507-13532 |
|
|
Keywords |
|
|
|
Abstract |
Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images. |
|
|
Address |
|
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG |
Approved |
no |
|
|
Call Number |
Admin @ si @ BSP2023 |
Serial |
3873 |
|
Permanent link to this record |
|
|
|
|
Author |
Siyang Song; Micol Spitale; Cheng Luo; German Barquero; Cristina Palmero; Sergio Escalera; Michel Valstar; Tobias Baur; Fabien Ringeval; Elisabeth Andre; Hatice Gunes |
![goto web page url](img/www.gif)
|
|
Title |
REACT2023: The First Multiple Appropriate Facial Reaction Generation Challenge |
Type |
Conference Article |
|
Year |
2023 |
Publication |
Proceedings of the 31st ACM International Conference on Multimedia |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
9620–9624 |
|
|
Keywords |
|
|
|
Abstract |
The Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learning techniques for generating human-appropriate facial reactions in various dyadic interaction scenarios, with all participants competing strictly under the same conditions. The goal of the challenge is to provide the first benchmark test set for multi-modal information processing and to foster collaboration among the audio, visual, and audio-visual behaviour analysis and behaviour generation (a.k.a generative AI) communities, to compare the relative merits of the approaches to automatic appropriate facial reaction generation under different spontaneous dyadic interaction conditions. This paper presents: (i) the novelties, contributions and guidelines of the REACT2023 challenge; (ii) the dataset utilized in the challenge; and (iii) the performance of the baseline systems on the two proposed sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2023. |
|
|
Address |
Otawa; Canada; October 2023 |
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
MM |
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ SSL2023 |
Serial |
3931 |
|
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 |
![goto web page url](img/www.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
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 |
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ACMW |
|
|
Notes |
HUPBA; no menciona |
Approved |
no |
|
|
Call Number |
Admin @ si @ GCD2022 |
Serial |
3801 |
|
Permanent link to this record |
|
|
|
|
Author |
Hugo Bertiche; Meysam Madadi; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
PBNS: Physically Based Neural Simulation for Unsupervised Garment Pose Space Deformation |
Type |
Journal Article |
|
Year |
2021 |
Publication |
ACM Transactions on Graphics |
Abbreviated Journal |
|
|
|
Volume |
40 |
Issue |
6 |
Pages |
1-14 |
|
|
Keywords |
|
|
|
Abstract |
We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for rigged garments through deep learning. Classical approaches rely on Physically Based Simulations (PBS) to animate clothes. These are general solutions that, given a sufficiently fine-grained discretization of space and time, can achieve highly realistic results. However, they are computationally expensive and any scene modification prompts the need of re-simulation. Linear Blend Skinning (LBS) with PSD offers a lightweight alternative to PBS, though, it needs huge volumes of data to learn proper PSD. We propose using deep learning, formulated as an implicit PBS, to unsupervisedly learn realistic cloth Pose Space Deformations in a constrained scenario: dressed humans. Furthermore, we show it is possible to train these models in an amount of time comparable to a PBS of a few sequences. To the best of our knowledge, we are the first to propose a neural simulator for cloth.
While deep-based approaches in the domain are becoming a trend, these are data-hungry models. Moreover, authors often propose complex formulations to better learn wrinkles from PBS data. Supervised learning leads to physically inconsistent predictions that require collision solving to be used. Also, dependency on PBS data limits the scalability of these solutions, while their formulation hinders its applicability and compatibility. By proposing an unsupervised methodology to learn PSD for LBS models (3D animation standard), we overcome both of these drawbacks. Results obtained show cloth-consistency in the animated garments and meaningful pose-dependant folds and wrinkles. Our solution is extremely efficient, handles multiple layers of cloth, allows unsupervised outfit resizing and can be easily applied to any custom 3D avatar. |
|
|
Address |
|
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
HUPBA; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ BME2021c |
Serial |
3643 |
|
Permanent link to this record |
|
|
|
|
Author |
Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Automatic Reminiscence Therapy for Dementia |
Type |
Conference Article |
|
Year |
2020 |
Publication |
10th ACM International Conference on Multimedia Retrieval |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
383-387 |
|
|
Keywords |
|
|
|
Abstract |
With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. |
|
|
Address |
Virtual; October 2020 |
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICRM |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
Admin @ si @ CGR2020 |
Serial |
3529 |
|
Permanent link to this record |
|
|
|
|
Author |
Maria Ines Torres; Javier Mikel Olaso; Cesar Montenegro; Riberto Santana; A.Vazquez; Raquel Justo; J.A.Lozano; Stephan Schogl; Gerard Chollet; Nazim Dugan; M.Irvine; N.Glackin; C.Pickard; Anna Esposito; Gennaro Cordasco; Alda Troncone; Dijana Petrovska Delacretaz; Aymen Mtibaa; Mohamed Amine Hmani; M.S.Korsnes; L.J.Martinussen; Sergio Escalera; C.Palmero Cantariño; Olivier Deroo; O.Gordeeva; Jofre Tenorio Laranga; E.Gonzalez Fraile; Begoña Fernandez Ruanova; A.Gonzalez Pinto |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
The EMPATHIC project: mid-term achievements |
Type |
Conference Article |
|
Year |
2019 |
Publication |
12th ACM International Conference on PErvasive Technologies Related to Assistive Environments |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
629-638 |
|
|
Keywords |
|
|
|
Abstract |
Maria Ines Torres; Javier Mikel Olaso, César Montenegro, Riberto Santana, A. Vázquez, Raquel Justo, J. A. Lozano, Stephan Schlögl, Gérard Chollet, Nazim Dugan, M. Irvine, N. Glackin, C. Pickard, Anna Esposito, Gennaro Cordasco, Alda Troncone, Dijana Petrovska-Delacrétaz, Aymen Mtibaa, Mohamed Amine Hmani, M. S. Korsnes, L. J. Martinussen, Sergio Escalera, C. Palmero Cantariño, Olivier Deroo, O. Gordeeva, Jofre Tenorio-Laranga, E. Gonzalez-Fraile, Begoña Fernández-Ruanova, A. Gonzalez-Pinto |
|
|
Address |
Rhodes Greece; June 2019 |
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
PETRA |
|
|
Notes |
HUPBA; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ TOM2019 |
Serial |
3325 |
|
Permanent link to this record |
|
|
|
|
Author |
Francesc Tanarro Marquez; Pau Gratacos Marti; F. Javier Sanchez; Joan Ramon Jimenez Minguell; Coen Antens; Enric Sala i Esteva |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
A device for monitoring condition of a railway supply |
Type |
Patent |
|
Year |
2012 |
Publication |
EP 2 404 777 A1 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
of a railway supply line when the supply line is in contact with a head of a pantograph of a vehicle in order to power said vehicle . The device includes a camera ( for monitoring parameters indicative of operating capability of said supply line.
The device is intended to monitor condition
tive of operating capability of said supply line. The device includes a reflective element. comprising a pattern , intended to be arranged onto the pantograph head . The camera is intended to be arranged on the vehicle (10) so as to register the pattern position regarding a vertical direction. |
|
|
Address |
|
|
|
Corporate Author |
ALSTOM Transport SA |
Thesis |
|
|
|
Publisher |
European Patent Office |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MV |
Approved |
no |
|
|
Call Number |
IAM @ iam @ MMS2012 |
Serial |
1854 |
|
Permanent link to this record |
|
|
|
|
Author |
Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Gabriel Brea-Martinez; Miquel Valls-Figols |
![goto web page url](img/www.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
|
|
Title |
The Baix Llobregat (BALL) Demographic Database, between Historical Demography and Computer Vision (nineteenth–twentieth centuries |
Type |
Book Chapter |
|
Year |
2019 |
Publication |
Nominative Data in Demographic Research in the East and the West: monograph |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
29-61 |
|
|
Keywords |
|
|
|
Abstract |
The Baix Llobregat (BALL) Demographic Database is an ongoing database project containing individual census data from the Catalan region of Baix Llobregat (Spain) during the nineteenth and twentieth centuries. The BALL Database is built within the project ‘NETWORKS: Technology and citizen innovation for building historical social networks to understand the demographic past’ directed by Alícia Fornés from the Center for Computer Vision and Joana Maria Pujadas-Mora from the Center for Demographic Studies, both at the Universitat Autònoma de Barcelona, funded by the Recercaixa program (2017–2019).
Its webpage is http://dag.cvc.uab.es/xarxes/.The aim of the project is to develop technologies facilitating massive digitalization of demographic sources, and more specifically the padrones (local censuses), in order to reconstruct historical ‘social’ networks employing computer vision technology. Such virtual networks can be created thanks to the linkage of nominative records compiled in the local censuses across time and space. Thus, digitized versions of individual and family lifespans are established, and individuals and families can be located spatially. |
|
|
Address |
|
|
|
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 |
978-5-7996-2656-3 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ PFL2019 |
Serial |
3351 |
|
Permanent link to this record |
|
|
|
|
Author |
Antonio Lopez; W. Niessen; Joan Serrat; K. Nikolay; B. Ter Haar Romeny; Juan J. Villanueva; M. Viergerver |
![goto web page url](img/www.gif)
|
|
Title |
New improvements in the multiscale analysis of trabecular bone patterns |
Type |
Book Chapter |
|
Year |
2000 |
Publication |
Pattern Recognition and Applications |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
251-260 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IOS Press |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ |
Serial |
3418 |
|
Permanent link to this record |
|
|
|
|
Author |
Clementine Decamps; Alexis Arnaud; Florent Petitprez; Mira Ayadi; Aurelia Baures; Lucile Armenoult; Sergio Escalera; Isabelle Guyon; Remy Nicolle; Richard Tomasini; Aurelien de Reynies; Jerome Cros; Yuna Blum; Magali Richard |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification |
Type |
Journal Article |
|
Year |
2021 |
Publication |
BMC Bioinformatics |
Abbreviated Journal |
|
|
|
Volume |
22 |
Issue |
|
Pages |
473 |
|
|
Keywords |
|
|
|
Abstract |
Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. |
|
|
Address |
|
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
HUPBA; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ DAP2021 |
Serial |
3650 |
|
Permanent link to this record |
|
|
|
|
Author |
Marc Bolaños; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Simultaneous Food Localization and Recognition |
Type |
Conference Article |
|
Year |
2016 |
Publication |
23rd International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
CoRR abs/1604.07953
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays – object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images. |
|
|
Address |
Cancun; Mexico; December 2016 |
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPR |
|
|
Notes |
MILAB; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ BoR2016 |
Serial |
2834 |
|
Permanent link to this record |
|
|
|
|
Author |
German Barquero; Sergio Escalera; Cristina Palmero |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Seamless Human Motion Composition with Blended Positional Encodings |
Type |
Miscellaneous |
|
Year |
2024 |
Publication |
Arxiv |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions. |
|
|
Address |
|
|
|
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 |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ BEP2024 |
Serial |
4022 |
|
Permanent link to this record |