| Home | [51–60] << 61 62 63 64 65 66 67 68 69 70 >> [71–80] |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Antonio Lopez; David Lloret; Joan Serrat | ||||
Title  |
Creaseness measures for CT and MR image registration. | Type | Miscellaneous | ||
| Year | 1998 | Publication | CVPR’98 , IEEE Computer Society, pgs.694–699 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy | ||||
| Address | Santa Barbara, USA. | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LLS1998a | Serial | 11 | ||
| Permanent link to this record | |||||
| Author | David Lloret; Antonio Lopez; Joan Serrat; Juan J. Villanueva | ||||
| Title |
Creaseness-based computer tomography and magnetic resonance registration: Comparison with the mutual information method. | Type | Miscellaneous | ||
| Year | 1999 | Publication | Journal of Electronic Imaging, 8(3):255–262 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | This paper describes a method which uses the skull as a landmark for automatic registration of computer tomography to magnetic resonance (MR) images. First, the skull is extracted from both images using a new creaseness operator. Then, the resulting creaseness images are used to build a hierarchic structure which permits a robust and fast search. We have justified experimentally the performance of several choices of our algorithm, and we have thoroughly tested its accuracy and robustness against the well-known mutual information method for five different pairs of images. We have found both comparable, and for certain MR images the proposed method achieves better performance. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LLS1999a | Serial | 189 | ||
| Permanent link to this record | |||||
| Author | Mohamed Ilyes Lakhal; Hakan Cevikalp; Sergio Escalera | ||||
| Title |
CRN: End-to-end Convolutional Recurrent Network Structure Applied to Vehicle Classification | Type | Conference Article | ||
| Year | 2018 | Publication | 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
| Volume | 5 | Issue | Pages | 137-144 | |
| Keywords | Vehicle Classification; Deep Learning; End-to-end Learning | ||||
| Abstract | Vehicle type classification is considered to be a central part of Intelligent Traffic Systems. In the recent years, deep learning methods have emerged in as being the state-of-the-art in many computer vision tasks. In this paper, we present a novel yet simple deep learning framework for the vehicle type classification problem. We propose an end-to-end trainable system, that combines convolution neural network for feature extraction and recurrent neural network as a classifier. The recurrent network structure is used to handle various types of feature inputs, and at the same time allows to produce a single or a set of class predictions. In order to assess the effectiveness of our solution, we have conducted a set of experiments in two public datasets, obtaining state of the art results. In addition, we also report results on the newly released MIO-TCD dataset. | ||||
| Address | Funchal; Madeira; Portugal; January 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | VISAPP | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ LCE2018a | Serial | 3094 | ||
| Permanent link to this record | |||||
| Author | Bojana Gajic; Ramon Baldrich | ||||
| Title |
Cross-domain fashion image retrieval | Type | Conference Article | ||
| Year | 2018 | Publication | CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) | Abbreviated Journal | |
| Volume | Issue | Pages | 19500-19502 | ||
| Keywords | |||||
| Abstract | Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products
in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task. |
||||
| Address | Salt Lake City, USA; 22 June 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | CIC; 600.087 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3709 | ||
| Permanent link to this record | |||||
| Author | Ajian Liu; Xuan Li; Jun Wan; Yanyan Liang; Sergio Escalera; Hugo Jair Escalante; Meysam Madadi; Yi Jin; Zhuoyuan Wu; Xiaogang Yu; Zichang Tan; Qi Yuan; Ruikun Yang; Benjia Zhou; Guodong Guo; Stan Z. Li | ||||
| Title |
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review | Type | Journal Article | ||
| Year | 2020 | Publication | IET Biometrics | Abbreviated Journal | BIO |
| Volume | 10 | Issue | 1 | Pages | 24-43 |
| Keywords | |||||
| Abstract | Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1,607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LLW2020b | Serial | 3523 | ||
| Permanent link to this record | |||||
| Author | Hugo Prol; Vincent Dumoulin; Luis Herranz | ||||
| Title |
Cross-Modulation Networks for Few-Shot Learning | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ PDH2018 | Serial | 3248 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title |
Cross-spectral image dehaze through a dense stacked conditional GAN based approach | Type | Conference Article | ||
| Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet Based System | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Infrared imaging; Dense; Stacked CGAN; Crossspectral; Convolutional networks | ||||
| Abstract | This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented
receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
||||
| Address | Las Palmas de Gran Canaria; November 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-5386-9385-8 | Medium | ||
| Area | Expedition | Conference | SITIS | ||
| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSV2018a | Serial | 3193 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title |
Cross-Spectral Image Patch Similarity using Convolutional Neural Network | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach. |
||||
| Address | San Sebastian; Spain; May 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECMSM | ||
| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017a | Serial | 2916 | ||
| Permanent link to this record | |||||
| Author | Michael Teutsch; Angel Sappa; Riad I. Hammoud | ||||
| Title |
Cross-Spectral Image Processing | Type | Book Chapter | ||
| Year | 2022 | Publication | Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 23-34 | ||
| Keywords | |||||
| Abstract | Although this book is on IR computer vision and its main focus lies on IR image and video processing and analysis, a special attention is dedicated to cross-spectral image processing due to the increasing number of publications and applications in this domain. In these cross-spectral frameworks, IR information is used together with information from other spectral bands to tackle some specific problems by developing more robust solutions. Tasks considered for cross-spectral processing are for instance dehazing, segmentation, vegetation index estimation, or face recognition. This increasing number of applications is motivated by cross- and multi-spectral camera setups available already on the market like for example smartphones, remote sensing multispectral cameras, or multi-spectral cameras for automotive systems or drones. In this chapter, different cross-spectral image processing techniques will be reviewed together with possible applications. Initially, image registration approaches for the cross-spectral case are reviewed: the registration stage is the first image processing task, which is needed to align images acquired by different sensors within the same reference coordinate system. Then, recent cross-spectral image colorization approaches, which are intended to colorize infrared images for different applications are presented. Finally, the cross-spectral image enhancement problem is tackled by including guided super resolution techniques, image dehazing approaches, cross-spectral filtering and edge detection. Figure 3.1 illustrates cross-spectral image processing stages as well as their possible connections. Table 3.1 presents some of the available public cross-spectral datasets generally used as reference data to evaluate cross-spectral image registration, colorization, enhancement, or exploitation results. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-031-00698-2 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MSIAU; MACO | Approved | no | ||
| Call Number | Admin @ si @ TSH2022b | Serial | 3805 | ||
| Permanent link to this record | |||||
| Author | M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa | ||||
| Title |
Cross-spectral image registration and fusion: an evaluation study | Type | Conference Article | ||
| Year | 2015 | Publication | 2nd International Conference on Machine Vision and Machine Learning | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | multispectral imaging; image registration; data fusion; infrared and visible spectra | ||||
| Abstract | This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
||||
| Address | Barcelona; July 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | MVML | ||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ CAV2015 | Serial | 2629 | ||
| Permanent link to this record | |||||
| Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo | ||||
| Title |
Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 17 | Issue | 4 | Pages | 873 |
| Keywords | |||||
| Abstract | This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ASA2017 | Serial | 2914 | ||
| Permanent link to this record | |||||
| Author | Naveen Onkarappa; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa | ||||
| Title |
Cross-spectral Stereo Correspondence using Dense Flow Fields | Type | Conference Article | ||
| Year | 2014 | Publication | 9th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | 3 | Issue | Pages | 613-617 | |
| Keywords | Cross-spectral Stereo Correspondence; Dense Optical Flow; Infrared and Visible Spectrum | ||||
| Abstract | This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach. | ||||
| Address | Lisboa; Portugal; January 2014 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | VISAPP | ||
| Notes | ADAS; 600.055; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ OAV2014 | Serial | 2477 | ||
| Permanent link to this record | |||||
| Author | Reuben Dorent; Aaron Kujawa; Marina Ivory; Spyridon Bakas; Nikola Rieke; Samuel Joutard; Ben Glocker; Jorge Cardoso; Marc Modat; Kayhan Batmanghelich; Arseniy Belkov; Maria Baldeon Calisto; Jae Won Choi; Benoit M. Dawant; Hexin Dong; Sergio Escalera; Yubo Fan; Lasse Hansen; Mattias P. Heinrich; Smriti Joshi; Victoriya Kashtanova; Hyeon Gyu Kim; Satoshi Kondo; Christian N. Kruse; Susana K. Lai-Yuen; Hao Li; Han Liu; Buntheng Ly; Ipek Oguz; Hyungseob Shin; Boris Shirokikh; Zixian Su; Guotai Wang; Jianghao Wu; Yanwu Xu; Kai Yao; Li Zhang; Sebastien Ourselin, | ||||
| Title |
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation | Type | Journal Article | ||
| Year | 2023 | Publication | Medical Image Analysis | Abbreviated Journal | MIA |
| Volume | 83 | Issue | Pages | 102628 | |
| Keywords | Domain Adaptation; Segmen tation; Vestibular Schwnannoma | ||||
| Abstract | Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality DA. The challenge's goal is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are performed using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore, we created an unsupervised cross-modality segmentation benchmark. The training set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137). A total of 16 teams submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice – VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice – VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ DKI2023 | Serial | 3706 | ||
| Permanent link to this record | |||||
| Author | Marwa Dhiaf; Mohamed Ali Souibgui; Kai Wang; Yuyang Liu; Yousri Kessentini; Alicia Fornes; Ahmed Cheikh Rouhou | ||||
| Title |
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large amount of labeled data. However, these methods are unable to capture new knowledge in an incremental fashion, where data is presented to the model sequentially, which is closer to the realistic scenario. In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. Our proposed framework is efficient in both computation and memory complexity. To demonstrate its effectiveness, we evaluate our method by transferring the learned model to diverse text recognition downstream tasks, including Latin and non-Latin scripts. As far as we know, this is the first application of continual self-supervised learning for handwritten text recognition. We attain state-of-the-art performance on English, Italian and Russian scripts, whilst adding only a few parameters per task. The code and trained models will be publicly available. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ DSW2023 | Serial | 3851 | ||
| Permanent link to this record | |||||
| Author | O. Rodriguez; J. Mauri; E Fernandez-Nofrerias; J. Lopez; A. Tovar; V. Valle; David Rotger; Petia Radeva | ||||
| Title |
Cuantificacion tridimensional de la longitud de segmentos coronarios a partir de secuencias de ecografia intracoronaria | Type | Journal | ||
| Year | 2003 | Publication | Revista Española de Cardiologia (IF: 0.959), 56(2), Congreso de las Enfermedades Cardiovasculares | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | Sevilla (Spain) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RMF2003g | Serial | 414 | ||
| Permanent link to this record | |||||