| Records |
| Author |
Eduardo Aguilar; Bogdan Raducanu; Petia Radeva; Joost Van de Weijer |
| Title |
Continual Evidential Deep Learning for Out-of-Distribution Detection |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE/CVF International Conference on Computer Vision (ICCV) Workshops -Visual Continual Learning workshop |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages  |
3444-3454 |
| Keywords |
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| Abstract |
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95. |
| Address |
Paris; France; October 2023 |
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ICCVW |
| Notes |
LAMP; MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ARR2023 |
Serial |
3841 |
| Permanent link to this record |
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| Author |
Eduardo Aguilar; Bogdan Raducanu; Petia Radeva; Joost Van de Weijer |
| Title |
Continual Evidential Deep Learning for Out-of-Distribution Detection |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3444-3454 |
| Keywords |
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| Abstract |
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-ofdistribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method 1, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95. |
| Address |
Paris; France; October 2023 |
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Conference |
ICCVW |
| Notes |
LAMP; MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ARR2023 |
Serial |
3974 |
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| Author |
Ozge Mercanoglu Sincan; Julio C. S. Jacques Junior; Sergio Escalera; Hacer Yalim Keles |
| Title |
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3467-3476 |
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| Abstract |
The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories. |
| Address |
Virtual; June 2021 |
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Conference |
CVPRW |
| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MJE2021 |
Serial |
3560 |
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera; Mohammad Sabokrou |
| Title |
Sign Language Production: A Review |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3472-3481 |
| Keywords |
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| Abstract |
Sign Language is the dominant yet non-primary form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. This survey aims to briefly summarize recent achievements in SLP, discussing their advantages, limitations, and future directions of research. |
| Address |
Virtual; June 2021 |
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Conference |
CVPRW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ RKE2021b |
Serial |
3603 |
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| Author |
Damian Sojka; Sebastian Cygert; Bartlomiej Twardowski; Tomasz Trzcinski |
| Title |
AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3491-3495 |
| Keywords |
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| Abstract |
Test-time adaptation is a promising research direction that allows the source model to adapt itself to changes in data distribution without any supervision. Yet, current methods are usually evaluated on benchmarks that are only a simplification of real-world scenarios. Hence, we propose to validate test-time adaptation methods using the recently introduced datasets for autonomous driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation methods struggle to effectively handle varying degrees of domain shift, often resulting in degraded performance that falls below that of the source model. We noticed that the root of the problem lies in the inability to preserve the knowledge of the source model and adapt to dynamically changing, temporally correlated data streams. Therefore, we enhance well-established self-training framework by incorporating a small memory buffer to increase model stability and at the same time perform dynamic adaptation based on the intensity of domain shift. The proposed method, named AR-TTA, outperforms existing approaches on both synthetic and more real-world benchmarks and shows robustness across a variety of TTA scenarios. |
| Address |
Paris; France; October 2023 |
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Conference |
ICCVW |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ SCT2023 |
Serial |
3943 |
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| Author |
David Vazquez; Antonio Lopez; Daniel Ponsa |
| Title |
Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
Type |
Conference Article |
| Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3492 - 3495 |
| Keywords |
Pedestrian Detection; Domain Adaptation; Virtual worlds |
| Abstract |
Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1). |
| Address |
Tsukuba Science City, Japan |
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| Publisher |
IEEE |
Place of Publication |
Tsukuba Science City, JAPAN |
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| ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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Conference |
ICPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ VLP2012 |
Serial |
1981 |
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| Author |
Fadi Dornaika; Bogdan Raducanu |
| Title |
Person-specific face shape estimation under varying head pose from single snapshots |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3496–3499 |
| Keywords |
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| Abstract |
This paper presents a new method for person-specific face shape estimation under varying head pose of a previously unseen person from a single image. We describe a featureless approach based on a deformable 3D model and a learned face subspace. The proposed approach is based on maximizing a likelihood measure associated with a learned face subspace, which is carried out by a stochastic and genetic optimizer. We conducted the experiments on a subset of Honda Video Database showing the feasibility and robustness of the proposed approach. For this reason, our approach could lend itself nicely to complex frameworks involving 3D face tracking and face gesture recognition in monocular videos. |
| Address |
Istanbul, Turkey |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
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Expedition |
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Conference |
ICPR |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ DoR2010b |
Serial |
1361 |
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| Author |
Valeriya Khan; Sebastian Cygert; Bartlomiej Twardowski; Tomasz Trzcinski |
| Title |
Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3496-3500 |
| Keywords |
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| Abstract |
In this work, we improve the generative replay in a continual learning setting. We notice that in VAE-based generative replay, the generated features are quite far from the original ones when mapped to the latent space. Therefore, we propose modifications that allow the model to learn and generate complex data. More specifically, we incorporate the distillation in latent space between the current and previous models to reduce feature drift. Additionally, a latent matching for the reconstruction and original data is proposed to improve generated features alignment. Further, based on the observation that the reconstructions are better for preserving knowledge, we add the cycling of generations through the previously trained model to make them closer to the original data. Our method outperforms other generative replay methods in various scenarios. |
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ICCVW |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ KCT2023 |
Serial |
3942 |
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| Author |
Filip Szatkowski; Mateusz Pyla; Marcin Przewięzlikowski; Sebastian Cygert; Bartłomiej Twardowski; Tomasz Trzcinski |
| Title |
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-Free Continual Learning |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3512-3517 |
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| Abstract |
In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KD-based methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher network when dealing with out-of-distribution data. This causes large errors in the KD loss component, leading to performance degradation in CIL. Inspired by recent test-time adaptation methods, we introduce Teacher Adaptation (TA), a method that concurrently updates the teacher and the main model during incremental training. Our method seamlessly integrates with KD-based CIL approaches and allows for consistent enhancement of their performance across multiple exemplar-free CIL benchmarks. |
| Address |
Paris; France; October 2023 |
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Conference |
ICCVW |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ |
Serial |
3944 |
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| Author |
Angel Sappa; Mohammad Rouhani |
| Title |
Efficient Distance Estimation for Fitting Implicit Quadric Surfaces |
Type |
Conference Article |
| Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3521–3524 |
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| Abstract |
This paper presents a novel approach for estimating the shortest Euclidean distance from a given point to the corresponding implicit quadric fitting surface. It first estimates the orthogonal orientation to the surface from the given point; then the shortest distance is directly estimated by intersecting the implicit surface with a line passing through the given point according to the estimated orthogonal orientation. The proposed orthogonal distance estimation is easily obtained without increasing computational complexity; hence it can be used in error minimization surface fitting frameworks. Comparisons of the proposed metric with previous approaches are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. Surfaces fitted by using the proposed geometric distance estimation and state of the art metrics are presented to show the viability of the proposed approach. |
| Address |
Cairo, Egypt |
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| ISSN |
1522-4880 |
ISBN |
978-1-4244-5653-6 |
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Conference |
ICIP |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ SaR2009 |
Serial |
1232 |
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| Author |
Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew Bagdanov; Alberto del Bimbo |
| Title |
Unsupervised scene adaptation for faster multi- scale pedestrian detection |
Type |
Conference Article |
| Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
3534 - 3539 |
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| Address |
Stockholm; Sweden; August 2014 |
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ICPR |
| Notes |
LAMP; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ BLK2014 |
Serial |
2519 |
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| Author |
Victor M. Campello; Polyxeni Gkontra; Cristian Izquierdo; Carlos Martin-Isla; Alireza Sojoudi; Peter M. Full; Klaus Maier-Hein; Yao Zhang; Zhiqiang He; Jun Ma; Mario Parreno; Alberto Albiol; Fanwei Kong; Shawn C. Shadden; Jorge Corral Acero; Vaanathi Sundaresan; Mina Saber; Mustafa Elattar; Hongwei Li; Bjoern Menze; Firas Khader; Christoph Haarburger; Cian M. Scannell; Mitko Veta; Adam Carscadden; Kumaradevan Punithakumar; Xiao Liu; Sotirios A. Tsaftaris; Xiaoqiong Huang; Xin Yang; Lei Li; Xiahai Zhuang; David Vilades; Martin L. Descalzo; Andrea Guala; Lucia La Mura; Matthias G. Friedrich; Ria Garg; Julie Lebel; Filipe Henriques; Mahir Karakas; Ersin Cavus; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Jose F. Rodriguez Palomares; Karim Lekadir |
| Title |
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
| Volume |
40 |
Issue |
12 |
Pages |
3543-3554 |
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| Abstract |
The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field. |
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HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CGI2021 |
Serial |
3653 |
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| Author |
Simone Balocco; O. Basset; G. Courbebaisse; E. Boni; Alejandro F. Frangi; P. Tortoli; C. Cachard |
| Title |
Estimation Of Viscoelastic Properties Of Vessel Walls Using a Computational Model and Doppler Ultrasound |
Type |
Journal Article |
| Year |
2010 |
Publication |
Physics in Medicine and Biology |
Abbreviated Journal |
PMB |
| Volume |
55 |
Issue |
12 |
Pages |
3557–3575 |
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| Abstract |
Human arteries affected by atherosclerosis are characterized by altered wall viscoelastic properties. The possibility of noninvasively assessing arterial viscoelasticity in vivo would significantly contribute to the early diagnosis and prevention of this disease. This paper presents a noniterative technique to estimate the viscoelastic parameters of a vascular wall Zener model. The approach requires the simultaneous measurement of flow variations and wall displacements, which can be provided by suitable ultrasound Doppler instruments. Viscoelastic parameters are estimated by fitting the theoretical constitutive equations to the experimental measurements using an ARMA parameter approach. The accuracy and sensitivity of the proposed method are tested using reference data generated by numerical simulations of arterial pulsation in which the physiological conditions and the viscoelastic parameters of the model can be suitably varied. The estimated values quantitatively agree with the reference values, showing that the only parameter affected by changing the physiological conditions is viscosity, whose relative error was about 27% even when a poor signal-to-noise ratio is simulated. Finally, the feasibility of the method is illustrated through three measurements made at different flow regimes on a cylindrical vessel phantom, yielding a parameter mean estimation error of 25%. |
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MILAB |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ BBC2010 |
Serial |
1312 |
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| Author |
Marc Masana; Tinne Tuytelaars; Joost Van de Weijer |
| Title |
Ternary Feature Masks: zero-forgetting for task-incremental learning |
Type |
Conference Article |
| Year |
2021 |
Publication |
34th IEEE Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3565-3574 |
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| Abstract |
We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue -- and show experimentally -- that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task; with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches. |
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Virtual; June 2021 |
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CVPRW |
| Notes |
LAMP; 600.120 |
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no |
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Admin @ si @ MTW2021 |
Serial |
3565 |
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| Author |
German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos |
| Title |
Articulated Particle Filter for Hand Tracking |
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Conference Article |
| Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
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3581 - 3585 |
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This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. |
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Tsukuba Science City, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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ADAS |
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Admin @ si @ RMG2012 |
Serial |
2031 |
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