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Author | David Berga; Xavier Otazu | ||||
Title | Computations of top-down attention by modulating V1 dynamics | Type | Conference Article | ||
Year | 2020 | Publication | Computational and Mathematical Models in Vision | Abbreviated Journal | |
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Address | St. Pete Beach; Florida; May 2020 | ||||
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Area | Expedition | Conference | MODVIS | ||
Notes | NEUROBIT | Approved | no | ||
Call Number | Admin @ si @ BeO2020a | Serial | 3376 | ||
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Author | David Berga; Xavier Otazu | ||||
Title | Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1 | Type | Journal Article | ||
Year | 2020 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
Volume | 417 | Issue | Pages | 270-289 | |
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Abstract | Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return, oculomotor and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of saccade amplitude and inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts. | ||||
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Notes | NEUROBIT | Approved | no | ||
Call Number | Admin @ si @ BeO2020c | Serial | 3444 | ||
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Author | David Berga; Marc Masana; Joost Van de Weijer | ||||
Title | Disentanglement of Color and Shape Representations for Continual Learning | Type | Conference Article | ||
Year | 2020 | Publication | ICML Workshop on Continual Learning | Abbreviated Journal | |
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Abstract | We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance. | ||||
Address | Virtual; July 2020 | ||||
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Area | Expedition | Conference | ICMLW | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ BMW2020 | Serial | 3506 | ||
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Author | Cristina Sanchez Montes; Jorge Bernal; Ana Garcia Rodriguez; Henry Cordova; Gloria Fernandez Esparrach | ||||
Title | Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia | Type | Journal Article | ||
Year | 2020 | Publication | Gastroenterología y Hepatología | Abbreviated Journal | GH |
Volume | 43 | Issue | 4 | Pages | 222-232 |
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Abstract | Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented. | ||||
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Notes | MV; | Approved | no | ||
Call Number | Admin @ si @ SBG2020 | Serial | 3404 | ||
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Author | Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa | ||||
Title | Fast CNN Stereo Depth Estimation through Embedded GPU Devices | Type | Journal Article | ||
Year | 2020 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 20 | Issue | 11 | Pages | 3249 |
Keywords | stereo matching; deep learning; embedded GPU | ||||
Abstract | Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices. | ||||
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Notes | MSIAU; 600.122 | Approved | no | ||
Call Number | Admin @ si @ AAN2020 | Serial | 3428 | ||
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Author | Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez | ||||
Title | Computing the Testing Error Without a Testing Set | Type | Conference Article | ||
Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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Abstract | Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach. |
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Address | Virtual CVPR | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CEM2020 | Serial | 3437 | ||
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Author | Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez | ||||
Title | Explainable Early Stopping for Action Unit Recognition | Type | Conference Article | ||
Year | 2020 | Publication | Faces and Gestures in E-health and welfare workshop | Abbreviated Journal | |
Volume | Issue | Pages | 693-699 | ||
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Abstract | A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop
training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it. In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it, we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs. |
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Address | Virtual; November 2020 | ||||
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Area | Expedition | Conference | FGW | ||
Notes | HUPBA; | Approved | no | ||
Call Number | Admin @ si @ CME2020 | Serial | 3514 | ||
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Author | Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez | ||||
Title | Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models | Type | Journal Article | ||
Year | 2020 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 128 | Issue | Pages | 1505–1536 | |
Keywords | Procedural generation; Human action recognition; Synthetic data; Physics | ||||
Abstract | Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos. | ||||
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Notes | ADAS; 600.124; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SGC2019 | Serial | 3303 | ||
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Author | Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir | ||||
Title | Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge | Type | Conference Article | ||
Year | 2020 | Publication | MYOPS challenge and workshop | Abbreviated Journal | |
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Address | Virtual; October 2020 | ||||
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Area | Expedition | Conference | MICCAIW | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ MAG2020 | Serial | 3518 | ||
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Author | Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl | ||||
Title | Decryption of historical manuscripts: the DECRYPT project | Type | Journal Article | ||
Year | 2020 | Publication | Cryptologia | Abbreviated Journal | CRYPT |
Volume | 44 | Issue | 6 | Pages | 545-559 |
Keywords | automatic decryption; cipher collection; historical cryptology; image transcription | ||||
Abstract | Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology. | ||||
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Notes | DAG; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ MEF2020 | Serial | 3347 | ||
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Author | B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols | ||||
Title | Knowledge graph based methods for record linkage | Type | Journal Article | ||
Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 136 | Issue | Pages | 127-133 | |
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Abstract | Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.
In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results. |
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Notes | DAG; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GRP2020 | Serial | 3453 | ||
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Author | Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana | ||||
Title | Saliency from High-Level Semantic Image Features | Type | Journal | ||
Year | 2020 | Publication | SN Computer Science | Abbreviated Journal | SN |
Volume | 1 | Issue | 4 | Pages | 1-12 |
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Abstract | Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS). | ||||
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Notes | LAMP; 600.120; 600.109; 600.106 | Approved | no | ||
Call Number | Admin @ si @ AWD2020 | Serial | 3503 | ||
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Author | Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez | ||||
Title | Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem | Type | Journal | ||
Year | 2020 | Publication | Mathematics | Abbreviated Journal | MATH |
Volume | 20 | Issue | 8(9) | Pages | 1595 |
Keywords | STEM education; Project-based learning; Coding; software tool | ||||
Abstract | In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view. |
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Address | September 2020 | ||||
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Notes | IAM; ISE | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3722 | ||
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Author | Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer | ||||
Title | Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches. | Type | Conference Article | ||
Year | 2020 | Publication | International Workshop on Artificial Intelligence for Healthcare Applications | Abbreviated Journal | |
Volume | 12661 | Issue | Pages | 476-489 | |
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Abstract | Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field. | ||||
Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | ICPRW | ||
Notes | DAG; 600.121; 600.140; | Approved | no | ||
Call Number | Admin @ si @ BCF2020 | Serial | 3508 | ||
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Author | Arnau Baro; Alicia Fornes; Carles Badal | ||||
Title | Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism | Type | Conference Article | ||
Year | 2020 | Publication | 17th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
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Abstract | Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. | ||||
Address | Virtual ICFHR; September 2020 | ||||
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Area | Expedition | Conference | ICFHR | ||
Notes | DAG; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BFB2020 | Serial | 3448 | ||
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