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| Author | Sergio Escalera; Stephane Ayache; Jun Wan; Meysam Madadi; Umut Guçlu; Xavier Baro | ||||
| Title | Inpainting and Denoising Challenges | Type | Book Whole | ||
| Year | 2019 | Publication  |
The Springer Series on Challenges in Machine Learning | Abbreviated Journal | |
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| Abstract | The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting.
Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting. |
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| Area | Expedition | Conference | |||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EAW2019 | Serial | 3398 | ||
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| Author | Hugo Jair Escalante; Sergio Escalera; Isabelle Guyon; Xavier Baro; Yagmur Gucluturk; Umut Guçlu; Marcel van Gerven | ||||
| Title | Explainable and Interpretable Models in Computer Vision and Machine Learning | Type | Book Whole | ||
| Year | 2018 | Publication |
The Springer Series on Challenges in Machine Learning | Abbreviated Journal | |
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| Abstract | This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: ·Evaluation and Generalization in Interpretable Machine Learning ·Explanation Methods in Deep Learning ·Learning Functional Causal Models with Generative Neural Networks ·Learning Interpreatable Rules for Multi-Label Classification ·Structuring Neural Networks for More Explainable Predictions ·Generating Post Hoc Rationales of Deep Visual Classification Decisions ·Ensembling Visual Explanations ·Explainable Deep Driving by Visualizing Causal Attention ·Interdisciplinary Perspective on Algorithmic Job Candidate Search ·Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions ·Inherent Explainability Pattern Theory-based Video Event Interpretations |
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EEG2018 | Serial | 3399 | ||
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| Author | Angel Sappa; M.A. Garcia | ||||
| Title | Generating compact representations of static scenes by means of 3D object hierarchies | Type | Journal | ||
| Year | 2007 | Publication |
The Visual Computer, 23(2): 143–154 | Abbreviated Journal | |
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ SaG2007a | Serial | 798 | ||
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| Author | Fernando Vilariño; Dimosthenis Karatzas; Marcos Catalan; Alberto Valcarcel | ||||
| Title | An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres | Type | Book Chapter | ||
| Year | 2015 | Publication |
The White Book on Public Library Network from Diputació de Barcelona | Abbreviated Journal | |
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| Notes | MV; DAG;SIAI | Approved | no | ||
| Call Number | Admin @ si @VKC2015 | Serial | 2798 | ||
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| Author | Albin Soutif; Marc Masana; Joost Van de Weijer; Bartlomiej Twardowski | ||||
| Title | On the importance of cross-task features for class-incremental learning | Type | Conference Article | ||
| Year | 2021 | Publication |
Theory and Foundation of continual learning workshop of ICML | Abbreviated Journal | |
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| Abstract | In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small. | ||||
| Address | Virtual; July 2021 | ||||
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| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ SMW2021 | Serial | 3588 | ||
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| Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui | ||||
| Title | Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation | Type | Conference Article | ||
| Year | 2021 | Publication |
Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) | Abbreviated Journal | |
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| Abstract | Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might no longer align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors, and propose a self regularization loss to decrease the negative impact of noisy neighbors. Furthermore, to aggregate information with more context, we consider expanded neighborhoods with small affinity values. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. Code is available in https://github.com/Albert0147/SFDA_neighbors. | ||||
| Address | Online; December 7-10, 2021 | ||||
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| Area | Expedition | Conference | NIPS | ||
| Notes | LAMP; 600.147; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3691 | ||
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| Author | Joan Serrat | ||||
| Title | Aplicacion del analisis de imagenes en radiologia. | Type | Miscellaneous | ||
| Year | 1995 | Publication |
To be published in a CD––ROM edited by AERFAI, the Spanish image Analysis and Pattern. | Abbreviated Journal | |
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ Ser1995 | Serial | 120 | ||
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| Author | Mikhail Mozerov; V. Kober; I.A. Ovseyevich | ||||
| Title | A Stereo Matching Algorithm with Global Smoothness Criterion | Type | Miscellaneous | ||
| Year | 2006 | Publication |
Topical Meeting on Optoinformatics / Information Photonics, 133–135 | Abbreviated Journal | |
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| Address | Saint-Petersburg (Russia) | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ MKO2006 | Serial | 675 | ||
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| Author | V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich | ||||
| Title | Pattern Recognition of Fragmented Objects with Adaptive Correlation Filters | Type | Miscellaneous | ||
| Year | 2006 | Publication |
Topical Meeting on Optoinformatics / Information Photonics, 150–151 | Abbreviated Journal | |
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| Address | Saint-Petersburg (Russia) | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ KMA2006b | Serial | 674 | ||
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| Author | Carles Fernandez; Jordi Gonzalez; Joao Manuel R. S. Taveres; Xavier Roca | ||||
| Title | Towards Ontological Cognitive System | Type | Book Chapter | ||
| Year | 2013 | Publication |
Topics in Medical Image Processing and Computational Vision | Abbreviated Journal | |
| Volume | 8 | Issue | Pages | 87-99 | |
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| Abstract | The increasing ubiquitousness of digital information in our daily lives has positioned video as a favored information vehicle, and given rise to an astonishing generation of social media and surveillance footage. This raises a series of technological demands for automatic video understanding and management, which together with the compromising attentional limitations of human operators, have motivated the research community to guide its steps towards a better attainment of such capabilities. As a result, current trends on cognitive vision promise to recognize complex events and self-adapt to different environments, while managing and integrating several types of knowledge. Future directions suggest to reinforce the multi-modal fusion of information sources and the communication with end-users. | ||||
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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
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| ISSN | 2212-9391 | ISBN | 978-94-007-0725-2 | Medium | |
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| Notes | ISE; 605.203; 302.018; 600.049 | Approved | no | ||
| Call Number | Admin @ si @ FGT2013 | Serial | 2287 | ||
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| Author | Salvatore Tabbone; Josep Llados | ||||
| Title | A Propos de la Reconnaissance de Documents Graphiques: Synthese et Perspectives | Type | Conference Article | ||
| Year | 2007 | Publication |
Traitement et Analyse de l’Information: Methodes et Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 247–258 | ||
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| Address | Hammamet (Tunis) | ||||
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| Area | Expedition | Conference | TAIMA’07 | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ TaL2007 | Serial | 890 | ||
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| Author | Daniel Ponsa; Joan Serrat; Antonio Lopez | ||||
| Title | On-board image-based vehicle detection and tracking | Type | Journal Article | ||
| Year | 2011 | Publication |
Transactions of the Institute of Measurement and Control | Abbreviated Journal | TIM |
| Volume | 33 | Issue | 7 | Pages | 783-805 |
| Keywords | vehicle detection | ||||
| Abstract | In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ PSL2011 | Serial | 1413 | ||
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| Author | Diego Velazquez; Pau Rodriguez; Alexandre Lacoste; Issam H. Laradji; Xavier Roca; Jordi Gonzalez | ||||
| Title | Evaluating Counterfactual Explainers | Type | Journal | ||
| Year | 2023 | Publication |
Transactions on Machine Learning Research | Abbreviated Journal | TMLR |
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| Keywords | Explainability; Counterfactuals; XAI | ||||
| Abstract | Explainability methods have been widely used to provide insight into the decisions made by statistical models, thus facilitating their adoption in various domains within the industry. Counterfactual explanation methods aim to improve our understanding of a model by perturbing samples in a way that would alter its response in an unexpected manner. This information is helpful for users and for machine learning practitioners to understand and improve their models. Given the value provided by counterfactual explanations, there is a growing interest in the research community to investigate and propose new methods. However, we identify two issues that could hinder the progress in this field. (1) Existing metrics do not accurately reflect the value of an explainability method for the users. (2) Comparisons between methods are usually performed with datasets like CelebA, where images are annotated with attributes that do not fully describe them and with subjective attributes such as ``Attractive''. In this work, we address these problems by proposing an evaluation method with a principled metric to evaluate and compare different counterfactual explanation methods. The evaluation method is based on a synthetic dataset where images are fully described by their annotated attributes. As a result, we are able to perform a fair comparison of multiple explainability methods in the recent literature, obtaining insights about their performance. We make the code public for the benefit of the research community. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ VRL2023 | Serial | 3891 | ||
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| Author | Josep Llados; Enric Marti | ||||
| Title | Interpretacio de dibuixos lineals mitjançant tècniques d isomorfisme entre grafs | Type | Conference Article | ||
| Year | 1995 | Publication |
Trobada de Joves Investigadors | Abbreviated Journal | |
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| Abstract | L’anàlisi de documents té com a objectiu la interpretació automàtica de documents impresos sobre paper, amb la finalitat d’obtenir una descripció simbòlica d’aquests, que permeti el seu emmagatzemament i posterior tractament computacional. Les tècniques basades en grafs relacionals d’atributs permeten representar de manera compacta la informació continguda en dibuixos lineals i mitjançant mecanismes d’isomorfisme entre grafs, reconèixer-hi certes estructures i d’aquesta manera, interpretar el document. En aquest treball es dóna una visió general de les tènciques de grafs aplicades al reconeixement visual d’objectes en problemes d’anàlisi de documents. Aquestes tècniques s’il·lustren amb un exemple de reconeixement de plànols dibuixats a mà alçada. Finalment es proposa la utilització de tècniques de Hough com a mecanisme per accelerar el procés de reconeixement aplicant un cert coneixement sobre el domini en el que es treballa | ||||
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| Notes | DAG;IAM | Approved | no | ||
| Call Number | IAM @ iam @ LlM1995 | Serial | 1578 | ||
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| Author | Petia Radeva; Enric Marti | ||||
| Title | Facial Features Segmentation by Model-Based Snakes. | Type | Miscellaneous | ||
| Year | 1995 | Publication |
Trobada de Joves Investigadors, IIIA. | Abbreviated Journal | |
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| Address | Bellaterra (Barcelona), Spain | ||||
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| Notes | MILAB; IAM | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RaM1995a | Serial | 130 | ||
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