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Jordi Vitria; J. Llacer |
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Title |
Reconstructing 3D light microscopic images using the EM algorithm |
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1996 |
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Pattern Recognition Letters |
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17 |
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14 |
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1491–1498 |
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OR;MV |
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BCNPCL @ bcnpcl @ ViL1996 |
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74 |
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Author |
Karla Lizbeth Caballero; Joel Barajas; Oriol Pujol |
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Title |
Reconstructing IVUS Images for an Accurate Tissue Classification |
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2007 |
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Proceedings of the Second International Conference on Computer Vision Theory and Applications |
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Special Sessions |
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113–119 |
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Barcelona (Spain) |
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VISAPP |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ CBP2007 |
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926 |
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Author |
Oriol Rodriguez-Leor; J. Mauri; Eduard Fernandez-Nofrerias; C. Garcia; R. Villuendas; Vicente del Valle; Debora Gil; Petia Radeva |
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Title |
Reconstruction of a spatio-temporal model of the intima layer from intravascular ultrasound sequences |
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2003 |
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European Heart Journal |
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ESC Congress |
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IAM;MILAB |
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IAM @ iam @ RMF2003c |
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1641 |
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Author |
Jordi Vitria; J. Llacer |
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Title |
Recovering brightness and depth from focus using the Expectation-Maximization Algorithm. |
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Miscellaneous |
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1995 |
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VI National Simposium on Pattern Recognition and image Analysis |
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Cordoba |
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OR;MV |
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BCNPCL @ bcnpcl @ ViL1995 |
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134 |
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Author |
Jordi Vitria; J. Llacer |
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Title |
Recovering Depth from Focus Using Iterative image Estimation Techniques. |
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Miscellaneous |
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1993 |
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Tech.Report BL–35158, Lawrence Berkeley Laboratory. |
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University of California; Berkeley; USA; |
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OR;MV |
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BCNPCL @ bcnpcl @ ViL1993 |
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142 |
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Author |
Matthias S. Keil; Gabriel Cristobal; Thorsten Hansen; Heiko Neumann |
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Title |
Recovering real-world images from single-scale boundaries with a novel filling-in architecture |
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2005 |
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Neural Networks 18(10):1319–1331 (IF: 1.665) |
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no |
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Admin @ si @ KCH2005 |
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576 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Antonio Lopez |
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Title |
Recovery of Surface Normals and Reflectance from Different Lighting Conditions |
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Conference Article |
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Year |
2008 |
Publication |
5th International Conference on Image Analysis and Recognition |
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Volume |
5112 |
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315–325 |
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ADAS |
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no |
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ADAS @ adas @ JSL2008c |
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1014 |
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Author |
Riccardo Del Chiaro; Bartlomiej Twardowski; Andrew Bagdanov; Joost Van de Weijer |
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Title |
Recurrent attention to transient tasks for continual image captioning |
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Conference Article |
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Year |
2020 |
Publication |
34th Conference on Neural Information Processing Systems |
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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. |
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virtual; December 2020 |
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NEURIPS |
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LAMP; 600.120 |
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no |
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Admin @ si @ CTB2020 |
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3484 |
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Author |
Cristina Palmero; Javier Selva; Mohammad Ali Bagheri; Sergio Escalera |
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Title |
Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues |
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Conference Article |
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Year |
2018 |
Publication |
29th British Machine Vision Conference |
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Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included. |
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Newcastle; UK; September 2018 |
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BMVC |
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HUPBA; no proj |
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no |
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Call Number |
Admin @ si @ PSB2018 |
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3208 |
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Author |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
Recurrent Comparator with attention models to detect counterfeit documents |
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Conference Article |
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2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms state-of-the-art classification approaches for counterfeit detection as demonstrated in the evaluation. |
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Sidney; Australia; September 2019 |
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ICDAR |
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DAG; 600.140; 600.121; 601.269 |
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no |
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Admin @ si @ BRL2019 |
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3456 |
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Author |
Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli |
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Title |
Recurrent Neural Networks for Remote Sensing Image Classification |
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Journal Article |
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2018 |
Publication |
IET Computer Vision |
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IETCV |
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12 |
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7 |
Pages |
1040 - 1045 |
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Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder–decoder framework. To the best of the authors’ knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset. |
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HUPBA; no proj |
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no |
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Admin @ si @ LÇE2018 |
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3119 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Juan J. Villanueva |
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Recursive Coarse-to-Fine Localization for fast Object Recognition |
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Conference Article |
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2010 |
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11th European Conference on Computer Vision |
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6313 |
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II |
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280–293 |
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Cascading techniques are commonly used to speed-up the scan of an image for object detection. However, cascades of detectors are slow to train due to the high number of detectors and corresponding thresholds to learn. Furthermore, they do not use any prior knowledge about the scene structure to decide where to focus the search. To handle these problems, we propose a new way to scan an image, where we couple a recursive coarse-to-fine refinement together with spatial constraints of the object location. For doing that we split an image into a set of uniformly distributed neighborhood regions, and for each of these we apply a local greedy search over feature resolutions. The neighborhood is defined as a scanning region that only one object can occupy. Therefore the best hypothesis is obtained as the location with maximum score and no thresholds are needed. We present an implementation of our method using a pyramid of HOG features and we evaluate it on two standard databases, VOC2007 and INRIA dataset. Results show that the Recursive Coarse-to-Fine Localization (RCFL) achieves a 12x speed-up compared to standard sliding windows. Compared with a cascade of multiple resolutions approach our method has slightly better performance in speed and Average-Precision. Furthermore, in contrast to cascading approach, the speed-up is independent of image conditions, the number of detected objects and clutter. |
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Crete (Greece) |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-15566-6 |
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ECCV |
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ISE |
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DAG @ dag @ PGB2010 |
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1438 |
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Author |
Maria Salamo; Inmaculada Rodriguez; Maite Lopez; Anna Puig; Simone Balocco; Mariona Taule |
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Recurso docente para la atención de la diversidad en el aula mediante la predicción de notas |
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2016 |
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ReVision |
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9 |
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1 |
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Aprendizaje automatico; Sistema de prediccion de notas; Herramienta docente |
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Desde la implantación del Espacio Europeo de Educación Superior (EEES) en los diferentes grados, se ha puesto de manifiesto la necesidad de utilizar diversos mecanismos que permitan tratar la diversidad en el aula, evaluando automáticamente y proporcionando una retroalimentación rápida tanto al alumnado como al profesorado sobre la evolución de los alumnos en una asignatura. En este artículo se presenta la evaluación de la exactitud en las predicciones de GRADEFORESEER, un recurso docente para la predicción de notas basado en técnicas de aprendizaje automático que permite evaluar la evolución del alumnado y estimar su nota final al terminar el curso. Este recurso se ha complementado con una interfaz de usuario para el profesorado que puede ser usada en diferentes plataformas software (sistemas operativos) y en cualquier asignatura de un grado en la que se utilice evaluación continuada. Además de la descripción del recurso, este artículo presenta los resultados obtenidos al aplicar el sistema de predicción en cuatro asignaturas de disciplinas distintas: Programación I (PI), Diseño de Software (DSW) del grado de Ingeniería Informática, Tecnologías de la Información y la Comunicación (TIC) del grado de Lingüística y la asignatura Fundamentos de Tecnología (FDT) del grado de Información y Documentación, todas ellas impartidas en la Universidad de Barcelona.
La capacidad predictiva se ha evaluado de forma binaria (aprueba o no) y según un criterio de rango (suspenso, aprobado, notable o sobresaliente), obteniendo mejores predicciones en los resultados evaluados de forma binaria. |
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MILAB; |
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Admin @ si @ SRL2016 |
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2820 |
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Author |
Aura Hernandez-Sabate; Debora Gil; J. Mauri; Petia Radeva |
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Title |
Reducing cardiac motion in IVUS sequences |
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Conference Article |
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2006 |
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Proceeding of Computers in Cardiology |
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33 |
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685-688 |
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Cardiac vessel displacement is a main artifact in IVUS sequences. It hinders visualization of the main structures in an appropriate orientation and alignment and affects extracting vessel measurements. In this paper, we present a novel approach for image sequence alignment based on spectral analysis, which removes rigid dynamics, preserving at the same time the vessel geometry. First, we suppress the translation by taking, for each frame, the center of mass of the image as origin of coordinates. In polar coordinates with such point as origin, the rotation appears as a horizontal displacement. The translation induces a phase shift in the Fourier coefficients of two consecutive polar images. We estimate the phase by adjusting a regression plane to the phases of the principal frequencies. Experiments show that the presented strategy suppress cardiac motion regardless of the acquisition device. 1. |
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IAM; MILAB |
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no |
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IAM @ iam @ HGM2006a |
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1554 |
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Author |
Javad Zolfaghari Bengar |
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Title |
Reducing Label Effort with Deep Active Learning |
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Book Whole |
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2021 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
Deep convolutional neural networks (CNNs) have achieved superior performance in many visual recognition applications, such as image classification, detection and segmentation. Training deep CNNs requires huge amounts of labeled data, which is expensive and labor intensive to collect. Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected
informative and/or representative samples. In this thesis we study several aspects of active learning including video object detection for autonomous driving systems, image classification on balanced and imbalanced datasets and the incorporation of self-supervised learning in active learning. We briefly describe our approach in each of these areas to reduce the labeling effort.
In chapter two we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our criterion is based on the estimated number of errors in terms of false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active
learning for video object detection in road scenes. Finally, we show that our
approach outperforms active learning baselines tested on two outdoor datasets.
In the next chapter we address the well-known problem of over confidence in the neural networks. As an alternative to network confidence, we propose a new informativeness-based active learning method that captures the learning dynamics of neural network with a metric called label-dispersion. This metric is low when the network consistently assigns the same label to the sample during the course of training and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
In chapter four, we tackle the problem of sampling bias in active learning methods on imbalanced datasets. Active learning is generally studied on balanced datasets where an equal amount of images per class is available. However, real-world datasets suffer from severe imbalanced classes, the so called longtail distribution. We argue that this further complicates the active learning process, since the imbalanced data pool can result in suboptimal classifiers. To address this problem in the context of active learning, we propose a general optimization framework that explicitly takes class-balancing into account. Results on three datasets show that the method is general (it can be combined with most existing active learning algorithms) and can be effectively applied to boost the performance of both informative and representative-based active learning methods. In addition, we show that also on balanced datasets our method generally results in a performance gain.
Another paradigm to reduce the annotation effort is self-training that learns from a large amount of unlabeled data in an unsupervised way and fine-tunes on few labeled samples. Recent advancements in self-training have achieved very impressive results rivaling supervised learning on some datasets. In the last chapter we focus on whether active learning and self supervised learning can benefit from each other.
We study object recognition datasets with several labeling budgets for the evaluations. Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high. |
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Address |
December 2021 |
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Thesis |
Ph.D. thesis |
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Publisher |
IMPRIMA |
Place of Publication |
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Editor |
Joost Van de Weijer;Bogdan Raducanu |
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ISBN |
978-84-122714-9-2 |
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Notes |
LAMP; |
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no |
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Call Number |
Admin @ si @ Zol2021 |
Serial |
3609 |
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