| 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 |
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| Volume |
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Issue |
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Pages |
3444-3454 |
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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 |
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LAMP; MILAB |
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no |
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Admin @ si @ ARR2023 |
Serial |
3841 |
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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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ICCVW |
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LAMP; MILAB |
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no |
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Admin @ si @ ARR2023 |
Serial |
3974 |
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| Author |
Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva |
| Title |
Uncertainty Modeling and Deep Learning Applied to Food Image Analysis |
Type |
Conference Article |
| Year |
2020 |
Publication |
13th International Joint Conference on Biomedical Engineering Systems and Technologies |
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Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis. |
| Address |
Villetta; Malta; February 2020 |
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BIODEVICES |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ANK2020 |
Serial |
3526 |
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| Author |
Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva |
| Title |
Bayesian deep learning for semantic segmentation of food images |
Type |
Journal Article |
| Year |
2022 |
Publication |
Computers and Electrical Engineering |
Abbreviated Journal |
CEE |
| Volume |
103 |
Issue |
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Pages |
108380 |
| Keywords |
Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis |
| Abstract |
Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. |
| Address |
October 2022 |
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| Publisher |
Science Direct |
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MILAB |
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no |
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Admin @ si @ ANR2022 |
Serial |
3763 |
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| Author |
Eduardo Aguilar; Beatriz Remeseiro; Marc Bolaños; Petia Radeva |
| Title |
Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Transactions on Multimedia |
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| Volume |
20 |
Issue |
12 |
Pages |
3266 - 3275 |
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| Abstract |
The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments. |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ARB2018 |
Serial |
3236 |
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| Author |
Eduard Vazquez; Theo Gevers; M. Lucassen; Joost Van de Weijer; Ramon Baldrich |
| Title |
Saliency of Color Image Derivatives: A Comparison between Computational Models and Human Perception |
Type |
Journal Article |
| Year |
2010 |
Publication |
Journal of the Optical Society of America A |
Abbreviated Journal |
JOSA A |
| Volume |
27 |
Issue |
3 |
Pages |
613–621 |
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| Abstract |
In this paper, computational methods are proposed to compute color edge saliency based on the information content of color edges. The computational methods are evaluated on bottom-up saliency in a psychophysical experiment, and on a more complex task of salient object detection in real-world images. The psychophysical experiment demonstrates the relevance of using information theory as a saliency processing model and that the proposed methods are significantly better in predicting color saliency (with a human-method correspondence up to 74.75% and an observer agreement of 86.8%) than state-of-the-art models. Furthermore, results from salient object detection confirm that an early fusion of color and contrast provide accurate performance to compute visual saliency with a hit rate up to 95.2%. |
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ISE;CIC |
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no |
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CAT @ cat @ VGL2010 |
Serial |
1275 |
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| Author |
Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell |
| Title |
Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures |
Type |
Journal Article |
| Year |
2011 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
33 |
Issue |
5 |
Pages |
917-930 |
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| Abstract |
The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost. |
| Address |
Los Alamitos; CA; USA; |
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IEEE Computer Society |
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0162-8828 |
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CIC |
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no |
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Admin @ si @ VBW2011 |
Serial |
1715 |
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| Author |
Eduard Vazquez; Ramon Baldrich; Javier Vazquez; Maria Vanrell |
| Title |
Topological histogram reduction towards colour segmentation |
Type |
Book Chapter |
| Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:55–62 |
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Gerona (Spain) |
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CIC |
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no |
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CAT @ cat @ VBV2007 |
Serial |
809 |
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| Author |
Eduard Vazquez; Ramon Baldrich |
| Title |
Colour Image Segmentation in Presence of Shadows |
Type |
Conference Article |
| Year |
2008 |
Publication |
4th European Conference on Colour in Graphics, Imaging and Vision Proceedings |
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383–387 |
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Terrassa (Spain) |
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CGIV08 |
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CAT;CIC |
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no |
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CAT @ cat @ VaB2008 |
Serial |
966 |
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| Author |
Eduard Vazquez; Ramon Baldrich |
| Title |
Non-supervised goodness measure for image segmentation |
Type |
Conference Article |
| Year |
2010 |
Publication |
Proceedings of The CREATE 2010 Conference |
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334–335 |
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Gjovik, Norway |
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CREATE |
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CIC |
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no |
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CAT @ cat @ VaB2010 |
Serial |
1299 |
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| Author |
Eduard Vazquez; Maria Vanrell |
| Title |
Eines per al desenvolupament de competencies de enginyeria en un assignatura de Intel·ligencia Artificial |
Type |
Miscellaneous |
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2008 |
Publication |
V Jornades d’Innovacio Docent (UAB) |
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Bellaterra (Spain) |
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CIC |
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no |
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CAT @ cat @ VaV2008 |
Serial |
1011 |
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| Author |
Eduard Vazquez; Joost Van de Weijer; Ramon Baldrich |
| Title |
Image Segmentation in the Presence of Shadows and Highligts |
Type |
Conference Article |
| Year |
2008 |
Publication |
10th European Conference on Computer Vision |
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5305 |
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1–14 |
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Marseille (France) |
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ECCV |
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CAT;CIC |
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no |
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CAT @ cat @ VVB2008b |
Serial |
1013 |
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| Author |
Eduard Vazquez; Francesc Tous; Ramon Baldrich; Maria Vanrell |
| Title |
n-Dimensional Distribution Reduction Preserving its Structure |
Type |
Book Chapter |
| Year |
2006 |
Publication |
Artificial Intelligence Research and Development, M. Polit et al. (Eds.), 146: 167–175 |
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IOS Press |
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CIC |
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no |
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CAT @ cat @ VTB2006a |
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681 |
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| Author |
Eduard Vazquez |
| Title |
Distribution Characterization using Topological Features. Application to Colour Image Processing |
Type |
Report |
| Year |
2007 |
Publication |
CVC Technical Report #107 |
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CVC (UAB) |
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no |
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Admin @ si @ Vaz2007a |
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823 |
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| Author |
Eduard Vazquez |
| Title |
Distribution Characterization using Topological Features. Application to Colour Image Processing |
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Report |
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2007 |
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CVC Technical Report # 107 |
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Master's thesis |
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Admin @ si @ Vaz2009 |
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1254 |
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