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| Author | Anna Salvatella | ||||
| Title | On texture description | Type | Report | ||
| Year | 2001 | Publication | CVC Technical Report #55 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ Sal2001 | Serial | 161 | ||
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| Author | Frederic Sampedro; Sergio Escalera | ||||
| Title | Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation | Type | Journal Article | ||
| Year | 2015 | Publication | IET Computer Vision | Abbreviated Journal | IETCV |
| Volume | 9 | Issue | 3 | Pages | 439 - 446 |
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| Abstract | In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches. | ||||
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| ISSN | 1751-9632 | ISBN | Medium | ||
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| Notes | HuPBA;MILAB | Approved | no | ||
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Admin @ si @ SaE2015 | Serial | 2551 | ||
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| Author | Maria Salamo; Sergio Escalera | ||||
| Title | Increasing Retrieval Quality in Conversational Recommenders | Type | Journal Article | ||
| Year | 2011 | Publication | IEEE Transactions on Knowledge and Data Engineering | Abbreviated Journal | TKDE |
| Volume | 99 | Issue | Pages | 1-1 | |
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| Abstract | IF JCR CCIA 2.286 2009 24/103
JCR Impact Factor 2010: 1.851 A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches |
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| Publisher | IEEE | Place of Publication | Editor | ||
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| ISSN | 1041-4347 | ISBN | Medium | ||
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| Notes | MILAB; HuPBA | Approved | no | ||
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Admin @ si @ SaE2011 | Serial | 1713 | ||
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| Author | Patricia Suarez; Angel Sappa; Dario Carpio; Henry Velesaca; Francisca Burgos; Patricia Urdiales | ||||
| Title | Deep Learning Based Shrimp Classification | Type | Conference Article | ||
| Year | 2022 | Publication | 17th International Symposium on Visual Computing | Abbreviated Journal | |
| Volume | 13598 | Issue | Pages | 36–45 | |
| Keywords | Pigmentation; Color space; Light weight network | ||||
| Abstract | This work proposes a novel approach based on deep learning to address the classification of shrimp (Pennaeus vannamei) into two classes, according to their level of pigmentation accepted by shrimp commerce. The main goal of this actual study is to support the shrimp industry in terms of price and process. An efficient CNN architecture is proposed to perform image classification through a program that could be set other in mobile devices or in fixed support in the shrimp supply chain. The proposed approach is a lightweight model that uses HSV color space shrimp images. A simple pipeline shows the most important stages performed to determine a pattern that identifies the class to which they belong based on their pigmentation. For the experiments, a database acquired with mobile devices of various brands and models has been used to capture images of shrimp. The results obtained with the images in the RGB and HSV color space allow for testing the effectiveness of the proposed model. | ||||
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| Area | Expedition | Conference | ISVC | ||
| Notes | MSIAU; no proj | Approved | no | ||
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Admin @ si @ SAC2022 | Serial | 3772 | ||
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| Author | Joaquin Salas; Wendy Avalos; Rafael Castañeda; Mario Maya | ||||
| Title | A machine-vision system to measure the parameters describing the performance of a Foucault pendulum | Type | Journal | ||
| Year | 2006 | Publication | Machine Vision and Applications | Abbreviated Journal | |
| Volume | 17 | Issue | 2 | Pages | 133–138 |
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| Notes | Approved | no | |||
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Admin @ si @ SAC2006 | Serial | 644 | ||
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| Author | F. Javier Sanchez; Jorge Bernal | ||||
| Title | Use of Software Tools for Real-time Monitoring of Learning Processes: Application to Compilers subject | Type | Conference Article | ||
| Year | 2018 | Publication | 4th International Conference of Higher Education Advances | Abbreviated Journal | |
| Volume | Issue | Pages | 1359-1366 | ||
| Keywords | Monitoring; Evaluation tool; Gamification; Student motivation | ||||
| Abstract | The effective implementation of the Higher European Education Area has meant a change regarding the focus of the learning process, being now the student at its very center. This shift of focus requires a strong involvement and fluent communication between teachers and students to succeed. Considering the difficulties associated to motivate students to take a more active role in the learning process, we explore how the use of a software tool can help both actors to improve the learning experience. We present a tool that can help students to obtain instantaneous feedback with respect to their progress in the subject as well as providing teachers with useful information about the evolution of knowledge acquisition with respect to each of the subject areas. We compare the performance achieved by students in two academic years: results show an improvement in overall performance which, after observing graphs provided by our tool, can be associated to an increase in students interest in the subject. | ||||
| Address | Valencia; June 2018 | ||||
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| Area | Expedition | Conference | HEAD | ||
| Notes | MV; no proj | Approved | no | ||
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Admin @ si @ SaB2018 | Serial | 3165 | ||
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| Author | J.M. Sanchez; X. Binefa | ||||
| Title | Color Normalization for Appearance Based Recognition of Video Key-frames. | Type | Miscellaneous | ||
| Year | 2000 | Publication | 15 th International Conference on Pattern Recognition, 1:815–818. | Abbreviated Journal | |
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| Address | Barcelona. | ||||
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| Notes | Approved | no | |||
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Admin @ si @ SaB2000 | Serial | 220 | ||
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| Author | J.M. Sanchez; X. Binefa | ||||
| Title | Color normalization for digital video processing | Type | Report | ||
| Year | 1999 | Publication | CVC Technical Report #37 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ SaB1999 | Serial | 525 | ||
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| Author | Dawid Rymarczyk; Joost van de Weijer; Bartosz Zielinski; Bartlomiej Twardowski | ||||
| Title | ICICLE: Interpretable Class Incremental Continual Learning. Dawid Rymarczyk | Type | Conference Article | ||
| Year | 2023 | Publication | 20th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1887-1898 | ||
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| Abstract | Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks. However, continual learning poses new challenges for interpretability, as the rationale behind model predictions may change over time, leading to interpretability concept drift. We address this problem by proposing Interpretable Class-InCremental LEarning (ICICLE), an exemplar-free approach that adopts a prototypical part-based approach. It consists of three crucial novelties: interpretability regularization that distills previously learned concepts while preserving user-friendly positive reasoning; proximity-based prototype initialization strategy dedicated to the fine-grained setting; and task-recency bias compensation devoted to prototypical parts. Our experimental results demonstrate that ICICLE reduces the interpretability concept drift and outperforms the existing exemplar-free methods of common class-incremental learning when applied to concept-based models. | ||||
| Address | Paris; France; October 2023 | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | LAMP | Approved | no | ||
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Admin @ si @ RWZ2023 | Serial | 3947 | ||
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| 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 | |
| Volume | Issue | Pages | 3703-3711 | ||
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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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| Area | Expedition | Conference | ICCV | ||
| Notes | LAMP; 600.068; 600.079 | Approved | no | ||
| Call Number |
Admin @ si @ RWB2015 | Serial | 2671 | ||
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| Author | A. Ruiz; Joost Van de Weijer; Xavier Binefa | ||||
| Title | Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization | Type | Conference Article | ||
| Year | 2014 | Publication | 25th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection. | ||||
| Address | Nottingham; UK; September 2014 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | LAMP; CIC; 600.074; 600.079 | Approved | no | ||
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Admin @ si @ RWB2014 | Serial | 2508 | ||
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| Author | Rafael E. Rivadeneira; Henry Velesaca; Angel Sappa | ||||
| Title | Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Signal-Image Technology & Internet-Based Systems | Abbreviated Journal | |
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| Abstract | This work proposes a novel approach that integrates super-resolution techniques with off-the-shelf object detection methods to tackle the problem of handling very low-resolution thermal images. The suggested approach begins by enhancing the low-resolution (LR) thermal images through a guided super-resolution strategy, leveraging a high-resolution (HR) visible spectrum image. Subsequently, object detection is performed on the high-resolution thermal image. The experimental results demonstrate tremendous improvements in comparison with both scenarios: when object detection is performed on the LR thermal image alone, as well as when object detection is conducted on the up-sampled LR thermal image. Moreover, the proposed approach proves highly valuable in camouflaged scenarios where objects might remain undetected in visible spectrum images. | ||||
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| Area | Expedition | Conference | SITIS | ||
| Notes | MSIAU | Approved | no | ||
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Admin @ si @ RVS2023 | Serial | 4010 | ||
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| Author | Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias | ||||
| Title | Understanding trained CNNs by indexing neuron selectivity | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 318-325 | |
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| Abstract | The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. | ||||
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| Notes | CIC; 600.087; 600.140; 600.118 | Approved | no | ||
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Admin @ si @ RVL2019 | Serial | 3310 | ||
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| Author | Mohammed Al Rawi; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting? | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 260-267 | ||
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| Abstract | Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scene-text images. Current word spotting approaches are designed to work on a single language and/or script. Building intelligent models that learn script-independent multilingual word-spotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet-152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a one-model script-independent multilingual word-spotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The one-model we propose performs on par with the multi-model language-specific word-spotting system, and thus, reduces the number of models needed for each script and/or language. | ||||
| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.129; 600.121 | Approved | no | ||
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Admin @ si @ RVK2019 | Serial | 3337 | ||
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| Author | Gabriela Ramirez; Esau Villatoro; Bogdan Ionescu; Hugo Jair Escalante; Sergio Escalera; Martha Larson; Henning Muller; Isabelle Guyon | ||||
| Title | Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contes | Type | Conference Article | ||
| Year | 2018 | Publication | Multimedia Information Processing for Personality and Social Networks Analysis (MIPPSNA 2018) | Abbreviated Journal | |
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| Address | Beijing; China; August 2018 | ||||
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| Area | Expedition | Conference | ICPRW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number |
Admin @ si @ RVI2018 | Serial | 3211 | ||
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