| Records |
Author  |
Robert Benavente |
| Title |
Dealing with colour variability: application to a colour naming task |
Type |
Report |
| Year |
1999 |
Publication |
CVC Technical Report #32 |
Abbreviated Journal |
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Issue |
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Pages |
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| Address |
CVC (UAB) |
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| Notes |
CIC |
Approved |
no |
| Call Number |
CAT @ cat @ Ben1999 |
Serial |
53 |
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| Author |
Robert Benavente |
| Title |
A Parametric Model for Computational Colour Naming |
Type |
Book Whole |
| Year |
2007 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
PhD Thesis |
| Abstract |
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| Address |
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| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Maria Vanrell |
| Language |
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Summary Language |
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| Notes |
CIC |
Approved |
no |
| Call Number |
CAT @ cat @ Ben2007 |
Serial |
1108 |
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| Author |
Riccardo Del Chiaro; Bartlomiej Twardowski; Andrew Bagdanov; Joost Van de Weijer |
| Title |
Recurrent attention to transient tasks for continual image captioning |
Type |
Conference Article |
| Year |
2020 |
Publication |
34th Conference on Neural Information Processing Systems |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper we take a systematic look at continual learning of LSTM-based models for image captioning. We propose an attention-based approach that explicitly accommodates the transient nature of vocabularies in continual image captioning tasks -- i.e. that task vocabularies are not disjoint. We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight egularization and knowledge distillation to recurrent continual learning problems. We apply our approaches to incremental image captioning problem on two new continual learning benchmarks we define using the MS-COCO and Flickr30 datasets. Our results demonstrate that RATT is able to sequentially learn five captioning tasks while incurring no forgetting of previously learned ones. |
| Address |
virtual; December 2020 |
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Conference |
NEURIPS |
| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ CTB2020 |
Serial |
3484 |
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| Author |
Ricardo Toledo; X. Orriols; X. Binefa; Petia Radeva; Jordi Vitria; Juan J. Villanueva |
| Title |
Tracking Elongated Structures using Statistical Snakes. |
Type |
Miscellaneous |
| Year |
2000 |
Publication |
Computer Vision and Pattern Recognition CVPR´00, 1:157–162. |
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Conference |
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| Notes |
OR;MILAB;ADAS;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ TOB2000 |
Serial |
339 |
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| Author |
Ricardo Toledo; X. Orriols; Petia Radeva; X. Binefa; Jordi Vitria; Cristina Cañero; Juan J. Villanueva |
| Title |
Eigensnakes for vessel segmentation in angiography. |
Type |
Conference Article |
| Year |
2000 |
Publication |
15 th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
4 |
Issue |
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Pages |
340-343 |
| Keywords |
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| Abstract |
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| Address |
Barcelona. |
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Conference |
ICPR |
| Notes |
OR;MILAB;ADAS;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ TOR2000 |
Serial |
235 |
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| Author |
Ricardo Toledo; Ramon Baldrich; Ernest Valveny; Petia Radeva |
| Title |
Enhancing snakes for vessel detection in angiography images. |
Type |
Miscellaneous |
| Year |
2002 |
Publication |
Proceedings of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 139–144. |
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Expedition |
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Conference |
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| Notes |
MILAB;DAG;CIC;ADAS |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ TBV2002 |
Serial |
300 |
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| Author |
Ricardo Toledo |
| Title |
Cardiac workstation and dynamic model to assist in coronary tree analysis. |
Type |
Book Whole |
| Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
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| Address |
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| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
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Place of Publication |
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Editor |
Petia Radeva;JuanJose Villanueva |
| Language |
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Summary Language |
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Original Title |
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Edition |
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ISBN |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ Tol2001 |
Serial |
166 |
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| Author |
Ricardo Dario Perez Principi; Cristina Palmero; Julio C. S. Jacques Junior; Sergio Escalera |
| Title |
On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signals |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
12 |
Issue |
3 |
Pages |
607-621 |
| Keywords |
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| Abstract |
Personality perception is implicitly biased due to many subjective factors, such as cultural, social, contextual, gender and appearance. Approaches developed for automatic personality perception are not expected to predict the real personality of the target, but the personality external observers attributed to it. Hence, they have to deal with human bias, inherently transferred to the training data. However, bias analysis in personality computing is an almost unexplored area. In this work, we study different possible sources of bias affecting personality perception, including emotions from facial expressions, attractiveness, age, gender, and ethnicity, as well as their influence on prediction ability for apparent personality estimation. To this end, we propose a multi-modal deep neural network that combines raw audio and visual information alongside predictions of attribute-specific models to regress apparent personality. We also analyse spatio-temporal aggregation schemes and the effect of different time intervals on first impressions. We base our study on the ChaLearn First Impressions dataset, consisting of one-person conversational videos. Our model shows state-of-the-art results regressing apparent personality based on the Big-Five model. Furthermore, given the interpretability nature of our network design, we provide an incremental analysis on the impact of each possible source of bias on final network predictions. |
| Address |
1 July-Sept. 2021 |
| Corporate Author |
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Original Title |
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Series Title |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ PPJ2019 |
Serial |
3312 |
| Permanent link to this record |
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| Author |
Ricard Coll; Alicia Fornes; Josep Llados |
| Title |
Graphological Analysis of Handwritten Text Documents for Human Resources Recruitment |
Type |
Conference Article |
| Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1081–1085 |
| Keywords |
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| Abstract |
The use of graphology in recruitment processes has become a popular tool in many human resources companies. This paper presents a model that links features from handwritten images to a number of personality characteristics used to measure applicant aptitudes for the job in a particular hiring scenario. In particular we propose a model of measuring active personality and leadership of the writer. Graphological features that define such a profile are measured in terms of document and script attributes like layout configuration, letter size, shape, slant and skew angle of lines, etc. After the extraction, data is classified using a neural network. An experimental framework with real samples has been constructed to illustrate the performance of the approach. |
| Address |
Barcelona, Spain |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Series Title |
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Series Issue |
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Edition |
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| ISSN |
1520-5363 |
ISBN |
978-1-4244-4500-4 |
Medium |
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| Area |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ CFL2009 |
Serial |
1221 |
| Permanent link to this record |
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| Author |
Ricard Borras; Agata Lapedriza; Laura Igual |
| Title |
Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition |
Type |
Conference Article |
| Year |
2012 |
Publication |
9th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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| Volume |
7325 |
Issue |
II |
Pages |
98-105 |
| Keywords |
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| Abstract |
This work presents DGait, a new gait database acquired with a depth camera. This database contains videos from 53 subjects walking in different directions. The intent of this database is to provide a public set to explore whether the depth can be used as an additional information source for gait classification purposes. Each video is labelled according to subject, gender and age. Furthermore, for each subject and view point, we provide initial and final frames of an entire walk cycle. On the other hand, we perform gait-based gender classification experiments with DGait database, in order to illustrate the usefulness of depth information for this purpose. In our experiments, we extract 2D and 3D gait features based on shape descriptors, and compare the performance of these features for gender identification, using a Kernel SVM. The obtained results show that depth can be an information source of great relevance for gait classification problems. |
| Address |
Aveiro, Portugal |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-31297-7 |
Medium |
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| Area |
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Expedition |
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Conference |
ICIAR |
| Notes |
OR; MILAB;MV |
Approved |
no |
| Call Number |
Admin @ si @ BLI2012 |
Serial |
2009 |
| Permanent link to this record |
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| Author |
Ricard Balague |
| Title |
Exploring the combination of color cues for intrinsic image decomposition |
Type |
Report |
| Year |
2014 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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| Volume |
178 |
Issue |
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Pages |
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| Keywords |
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| Abstract |
Intrinsic image decomposition is a challenging problem that consists in separating an image into its physical characteristics: reflectance and shading. This problem can be solved in different ways, but most methods have combined information from several visual cues. In this work we describe an extension of an existing method proposed by Serra et al. which considers two color descriptors and combines them by means of a Markov Random Field. We analyze in depth the weak points of the method and we explore more possibilities to use in both descriptors. The proposed extension depends on the combination of the cues considered to overcome some of the limitations of the original method. Our approach is tested on the MIT dataset and Beigpour et al. dataset, which contain images of real objects acquired under controlled conditions and synthetic images respectively, with their corresponding ground truth. |
| Address |
UAB; September 2014 |
| Corporate Author |
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Thesis |
Master's thesis |
| Publisher |
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Conference |
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| Notes |
CIC; 600.074 |
Approved |
no |
| Call Number |
Admin @ si @ Bal2014 |
Serial |
2579 |
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| Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera |
| Title |
Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Transactions on Circuits and Systems for Video Technology |
Abbreviated Journal |
TCSVT |
| Volume |
29 |
Issue |
6 |
Pages |
1729-1740 |
| Keywords |
Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding |
| Abstract |
Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset. |
| Address |
June 2019, |
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| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ AAK2018 |
Serial |
3213 |
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| Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera |
| Title |
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation |
Type |
Conference Article |
| Year |
2020 |
Publication |
Bioimage computation workshop |
Abbreviated Journal |
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| Address |
Virtual; August 2020 |
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Conference |
ECCVW |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ AAF2020 |
Serial |
3520 |
| Permanent link to this record |
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| Author |
Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera |
| Title |
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions |
Type |
Conference Article |
| Year |
2019 |
Publication |
Visual Recognition for Medical Images workshop |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
406-415 |
| Keywords |
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| Abstract |
In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance. |
| Address |
Seul; Korea; October 2019 |
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Conference |
ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ AAF2019 |
Serial |
3324 |
| Permanent link to this record |
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| Author |
Reza Azad; Afshin Bozorgpour; Maryam Asadi-Aghbolaghi; Dorit Merhof; Sergio Escalera |
| Title |
Deep Frequency Re-Calibration U-Net for Medical Image Segmentation |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3274-3283 |
| Keywords |
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| Abstract |
We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. |
| Address |
VIRTUAL; October 2021 |
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Edition |
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Expedition |
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Conference |
ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ABA2021 |
Serial |
3645 |
| Permanent link to this record |
