| Records |
Author  |
Adria Ruiz; Joost Van de Weijer; Xavier Binefa |
| Title |
From emotions to action units with hidden and semi-hidden-task learning |
Type |
Conference Article |
| Year |
2015 |
Publication |
16th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3703-3711 |
| Keywords |
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| Abstract |
Limited annotated training data is a challenging problem in Action Unit recognition. In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers. For this purpose, we propose a novel learning framework: Hidden-Task Learning. HTL aims to learn a set of Hidden-Tasks (Action Units)for which samples are not available but, in contrast, training data is easier to obtain from a set of related VisibleTasks (Facial Expressions). To that end, HTL is able to exploit prior knowledge about the relation between Hidden and Visible-Tasks. In our case, we base this prior knowledge on empirical psychological studies providing statistical correlations between Action Units and universal facial expressions. Additionally, we extend HTL to Semi-Hidden Task Learning (SHTL) assuming that Action Unit training samples are also provided. Performing exhaustive experiments over four different datasets, we show that HTL and SHTL improve the generalization ability of AU classifiers by training them with additional facial expression data. Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training. |
| Address |
Santiago de Chile; Chile; December 2015 |
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Conference |
ICCV |
| Notes |
LAMP; 600.068; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ RWB2015 |
Serial |
2671 |
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| Author |
Adrian Galdran; Aitor Alvarez-Gila; Alessandro Bria; Javier Vazquez; Marcelo Bertalmio |
| Title |
On the Duality Between Retinex and Image Dehazing |
Type |
Conference Article |
| Year |
2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
8212–8221 |
| Keywords |
Image color analysis; Task analysis; Atmospheric modeling; Computer vision; Computational modeling; Lighting |
| Abstract |
Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem. |
| Address |
Salt Lake City; USA; June 2018 |
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Conference |
CVPR |
| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ GAB2018 |
Serial |
3146 |
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| Author |
Adriana Romero; Carlo Gatta |
| Title |
Do We Really Need All These Neurons? |
Type |
Conference Article |
| Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
7887 |
Issue |
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Pages |
460--467 |
| Keywords |
Retricted Boltzmann Machine; hidden units; unsupervised learning; classification |
| Abstract |
Restricted Boltzmann Machines (RBMs) are generative neural networks that have received much attention recently. In particular, choosing the appropriate number of hidden units is important as it might hinder their representative power. According to the literature, RBM require numerous hidden units to approximate any distribution properly. In this paper, we present an experiment to determine whether such amount of hidden units is required in a classification context. We then propose an incremental algorithm that trains RBM reusing the previously trained parameters using a trade-off measure to determine the appropriate number of hidden units. Results on the MNIST and OCR letters databases show that using a number of hidden units, which is one order of magnitude smaller than the literature estimate, suffices to achieve similar performance. Moreover, the proposed algorithm allows to estimate the required number of hidden units without the need of training many RBM from scratch. |
| Address |
Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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LNCS |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-38627-5 |
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Conference |
IbPRIA |
| Notes |
MILAB; 600.046 |
Approved |
no |
| Call Number |
Admin @ si @ RoG2013 |
Serial |
2311 |
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| Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
| Title |
Unsupervised Deep Feature Extraction Of Hyperspectral Images |
Type |
Conference Article |
| Year |
2014 |
Publication |
6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing |
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Issue |
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Pages |
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| Keywords |
Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification |
| Abstract |
This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features. |
| Address |
Lausanne; Switzerland; June 2014 |
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Conference |
WHISPERS |
| Notes |
MILAB; LAMP; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ RGC2014 |
Serial |
2513 |
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| Author |
Adriana Romero; Nicolas Ballas; Samira Ebrahimi Kahou; Antoine Chassang; Carlo Gatta; Yoshua Bengio |
| Title |
FitNets: Hints for Thin Deep Nets |
Type |
Conference Article |
| Year |
2015 |
Publication |
3rd International Conference on Learning Representations ICLR2015 |
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Issue |
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Pages |
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| Keywords |
Computer Science ; Learning; Computer Science ;Neural and Evolutionary Computing |
| Abstract |
While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network. |
| Address |
San Diego; CA; May 2015 |
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Conference |
ICLR |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ RBK2015 |
Serial |
2593 |
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| Author |
Adriana Romero; Simeon Petkov; Carlo Gatta; M.Sabate; Petia Radeva |
| Title |
Efficient automatic segmentation of vessels |
Type |
Conference Article |
| Year |
2012 |
Publication |
16th Conference on Medical Image Understanding and Analysis |
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| Address |
Swansea, United Kingdom |
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Conference |
MIUA |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ |
Serial |
2137 |
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| Author |
Agata Lapedriza; David Masip; David Sanchez |
| Title |
Emotions Classification using Facial Action Units Recognition |
Type |
Conference Article |
| Year |
2014 |
Publication |
17th International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
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| Volume |
269 |
Issue |
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Pages |
55-64 |
| Keywords |
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| Abstract |
In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection. |
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ISBN |
978-1-61499-451-0 |
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Conference |
CCIA |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ LMS2014 |
Serial |
2622 |
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| Author |
Agata Lapedriza; David Masip; Jordi Vitria |
| Title |
A Hierarchical Approach for Multi-task Logistic Regression |
Type |
Conference Article |
| Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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| Volume |
4478 |
Issue |
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Pages |
258–265 |
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| Address |
Girona (Spain) |
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Editor |
J. Marti et al. |
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LNCS |
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Conference |
IbPRIA |
| Notes |
OR; MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ LMV2007a |
Serial |
902 |
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| Author |
Agata Lapedriza; David Masip; Jordi Vitria |
| Title |
Subject Recognition Using a New Approach for Feature Extraction |
Type |
Conference Article |
| Year |
2008 |
Publication |
3rd International Conference on Computer Vision Theory and Applications |
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| Volume |
2 |
Issue |
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Pages |
61–66 |
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| Address |
Madeira (Portugal) |
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Conference |
VISAPP |
| Notes |
OR; MV |
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no |
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BCNPCL @ bcnpcl @ LMV2008a |
Serial |
980 |
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| Author |
Agata Lapedriza; David Masip; Jordi Vitria |
| Title |
On the Use of Independent Tasks for Face Recognition |
Type |
Conference Article |
| Year |
2008 |
Publication |
IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Issue |
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Pages |
1–6 |
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Conference |
CVPR |
| Notes |
OR; MV |
Approved |
no |
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BCNPCL @ bcnpcl @ LMV2008b |
Serial |
1043 |
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| Author |
Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
| Title |
High-Level Clothes Description Based on Colour-Texture and Structural Features |
Type |
Conference Article |
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2003 |
Publication |
1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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Palma de Mallorca |
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DAG;CIC |
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no |
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CAT @ cat @ BTL2003b |
Serial |
369 |
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| Author |
Agnes Borras; Josep Llados |
| Title |
A Multi-Scale Layout Descriptor Based on Delaunay Triangulation for Image Retrieval |
Type |
Conference Article |
| Year |
2008 |
Publication |
3rd International Conference on Computer Vision Theory and Applications VISAPP (2) 2008 |
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2 |
Issue |
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Pages |
139-144 |
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| Address |
Funchal, Madeira (Portugal) |
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DAG |
Approved |
no |
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DAG @ dag @ BoL2008 |
Serial |
981 |
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| Author |
Agnes Borras; Josep Llados |
| Title |
Corest: A measure of color and space stability to detect salient regions according to human criteria |
Type |
Conference Article |
| Year |
2009 |
Publication |
5th International Conference on Computer Vision Theory and Applications |
Abbreviated Journal |
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Issue |
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Pages |
204-209 |
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| Address |
Lisboa, Portugal |
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Edition |
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ISBN |
978-989-8111-69-2 |
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Conference |
VISAPP |
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DAG |
Approved |
no |
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DAG @ dag @ BoL2009 |
Serial |
1225 |
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| Author |
Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva |
| Title |
Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods |
Type |
Conference Article |
| Year |
2022 |
Publication |
29th IEEE International Conference on Image Processing |
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Issue |
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Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring |
| Abstract |
Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans. |
| Address |
Bordeaux; France; October 2022 |
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Conference |
ICIP |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ SBC2022 |
Serial |
3789 |
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| Author |
Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Polyxeni Gkontra; Karim Lekadir; Gloria Menegaz; Petia Radeva |
| Title |
A new scheme for the assessment of the robustness of Explainable Methods Applied to Brain Age estimation |
Type |
Conference Article |
| Year |
2021 |
Publication |
34th International Symposium on Computer-Based Medical Systems |
Abbreviated Journal |
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Issue |
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Pages |
492-497 |
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| Abstract |
Deep learning methods show great promise in a range of settings including the biomedical field. Explainability of these models is important in these fields for building end-user trust and to facilitate their confident deployment. Although several Machine Learning Interpretability tools have been proposed so far, there is currently no recognized evaluation standard to transfer the explainability results into a quantitative score. Several measures have been proposed as proxies for quantitative assessment of explainability methods. However, the robustness of the list of significant features provided by the explainability methods has not been addressed. In this work, we propose a new proxy for assessing the robustness of the list of significant features provided by two explainability methods. Our validation is defined at functionality-grounded level based on the ranked correlation statistical index and demonstrates its successful application in the framework of brain aging estimation. We assessed our proxy to estimate brain age using neuroscience data. Our results indicate small variability and high robustness in the considered explainability methods using this new proxy. |
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Conference |
CBMS |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ SBZ2021 |
Serial |
3629 |
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