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| Author | Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title | Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning | Type | Conference Article | ||
| Year | 2019 | Publication | 13th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 80-85 | ||
| Keywords | Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning | ||||
| Abstract | With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of sketches, showing promising results. |
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| Address | Sydney; Australia; September 2019 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.140; 601.302; 600.121 | Approved | no | ||
Call Number  |
Admin @ si @ BRF2019 | Serial | 3354 | ||
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| Author | Arnau Baro; Pau Riba; Alicia Fornes | ||||
| Title | A Starting Point for Handwritten Music Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 1st International Workshop on Reading Music Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 5-6 | ||
| Keywords | Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA | ||||
| Abstract | In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. | ||||
| Address | Paris; France; September 2018 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | WORMS | ||
| Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ BRF2018 | Serial | 3223 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Pau Riba; Alicia Fornes | ||||
| Title | Towards the recognition of compound music notes in handwritten music scores | Type | Conference Article | ||
| Year | 2016 | Publication | 15th international conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work we focus on this second problem and propose a method based on perceptual grouping for the recognition of compound music notes. Our method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition (OMR) software. Given that our method is learning-free, the obtained results are promising. | ||||
| Address | Shenzhen; China; October 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 2167-6445 | ISBN | Medium | ||
| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.097 | Approved | no | ||
| Call Number |
Admin @ si @ BRF2016 | Serial | 2903 | ||
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| Author | M. Bressan | ||||
| Title | Un analisis de viabilidad para la confeccion semisupervisada de un mapa de usos del suelo de Catalunya | Type | Report | ||
| Year | 2001 | Publication | CVC Technical Report #58 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Address | CVC (UAB) | ||||
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| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number |
Admin @ si @ Bre2001 | Serial | 182 | ||
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| Author | M. Bressan | ||||
| Title | Independent modes of variation in Point Distribution models | Type | Report | ||
| Year | 2000 | Publication | CVC Technical Report #48 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number |
Admin @ si @ Bre2000 | Serial | 349 | ||
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| Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
| Title | From Optical Music Recognition to Handwritten Music Recognition: a Baseline | Type | Journal Article | ||
| Year | 2019 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 123 | Issue | Pages | 1-8 | |
| Keywords | |||||
| Abstract | Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community. | ||||
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| Area | Expedition | Conference | |||
| Notes | DAG; 600.097; 601.302; 601.330; 600.140; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ BRC2019 | Serial | 3275 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
| Title | Optical Music Recognition by Long Short-Term Memory Networks | Type | Book Chapter | ||
| Year | 2018 | Publication | Graphics Recognition. Current Trends and Evolutions | Abbreviated Journal | |
| Volume | 11009 | Issue | Pages | 81-95 | |
| Keywords | Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory | ||||
| Abstract | Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. | ||||
| Address | |||||
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| Publisher | Springer | Place of Publication | Editor | A. Fornes, B. Lamiroy | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-030-02283-9 | Medium | ||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ BRC2018 | Serial | 3227 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
| Title | Optical Music Recognition by Recurrent Neural Networks | Type | Conference Article | ||
| Year | 2017 | Publication | 14th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 25-26 | ||
| Keywords | Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory | ||||
| Abstract | Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ BRC2017 | Serial | 3056 | ||
| Permanent link to this record | |||||
| Author | Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier | ||||
| Title | Delaunay triangulation-based features for Camera-based document image retrieval system | Type | Conference Article | ||
| Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 1-6 | ||
| Keywords | Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing | ||||
| Abstract | In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. | ||||
| Address | Santorini; Greece; April 2016 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.061; 600.084; 600.077 | Approved | no | ||
| Call Number |
Admin @ si @ BRC2016 | Serial | 2757 | ||
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| Author | Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera | ||||
| Title | Error-Correcting Factorization | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 40 | Issue | Pages | 2388-2401 | |
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| Abstract | Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number |
Admin @ si @ BPT2018 | Serial | 3015 | ||
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| Author | Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados | ||||
| Title | I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation | Type | Conference Article | ||
| Year | 2023 | Publication | 21st International Graphonomics Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 136–148 | ||
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| Abstract | During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation. | ||||
| Address | Evora; Portugal; October 2023 | ||||
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| Area | Expedition | Conference | IGS | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ BPG2023 | Serial | 3838 | ||
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| Author | Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez | ||||
| Title | Single image super-resolution based on directional variance attention network | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 133 | Issue | Pages | 108997 | |
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| Abstract | Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neural networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies simultaneously for more discriminative representations. Furthermore, we propose a residual attention feature group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ BPF2023 | Serial | 3861 | ||
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| Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva | ||||
| Title | Egocentric video description based on temporally-linked sequences | Type | Journal Article | ||
| Year | 2018 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal | JVCIR |
| Volume | 50 | Issue | Pages | 205-216 | |
| Keywords | egocentric vision; video description; deep learning; multi-modal learning | ||||
| Abstract | Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description. |
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number |
Admin @ si @ BPC2018 | Serial | 3109 | ||
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| Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva | ||||
| Title | VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering | Type | Conference Article | ||
| Year | 2017 | Publication | 8th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks | ||||
| Abstract | In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. | ||||
| Address | Faro; Portugal; June 2017 | ||||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number |
Admin @ si @ BPC2017 | Serial | 2939 | ||
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| Author | Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera | ||||
| Title | Introducing the Separability Matrix for Error Correcting Output Codes Coding | Type | Conference Article | ||
| Year | 2011 | Publication | 10th International Conference on Multiple Classifier Systems | Abbreviated Journal | |
| Volume | 6713 | Issue | Pages | 227-236 | |
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| Abstract | Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results. | ||||
| Address | Napoles, Italy | ||||
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| Publisher | Springer-Verlag Berlin, Heidelberg | Place of Publication | Editor | Carlo Sansone; Josef Kittler; Fabio Roli | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-21556-8 | Medium | |
| Area | Expedition | Conference | MCS | ||
| Notes | MILAB; OR;HuPBA;MV | Approved | no | ||
| Call Number |
Admin @ si @ BPB2011b | Serial | 1887 | ||
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