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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 (up) 34th IEEE Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal
Volume Issue Pages 3565-3574
Keywords
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.
Address Virtual; June 2021
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Area Expedition Conference CVPRW
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ MTW2021 Serial 3565
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Author Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Polyxeni Gkontra; Karim Lekadir; Gloria Menegaz; Petia Radeva
Title A new scheme for the assessment of the robustness of Explainable Methods Applied to Brain Age estimation Type Conference Article
Year 2021 Publication (up) 34th International Symposium on Computer-Based Medical Systems Abbreviated Journal
Volume Issue Pages 492-497
Keywords
Abstract Deep learning methods show great promise in a range of settings including the biomedical field. Explainability of these models is important in these fields for building end-user trust and to facilitate their confident deployment. Although several Machine Learning Interpretability tools have been proposed so far, there is currently no recognized evaluation standard to transfer the explainability results into a quantitative score. Several measures have been proposed as proxies for quantitative assessment of explainability methods. However, the robustness of the list of significant features provided by the explainability methods has not been addressed. In this work, we propose a new proxy for assessing the robustness of the list of significant features provided by two explainability methods. Our validation is defined at functionality-grounded level based on the ranked correlation statistical index and demonstrates its successful application in the framework of brain aging estimation. We assessed our proxy to estimate brain age using neuroscience data. Our results indicate small variability and high robustness in the considered explainability methods using this new proxy.
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference CBMS
Notes MILAB; no proj Approved no
Call Number Admin @ si @ SBZ2021 Serial 3629
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Author Josep Llados
Title The 5G of Document Intelligence Type Conference Article
Year 2021 Publication (up) 3rd Workshop on Future of Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages
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Abstract
Address Lausanne; Suissa; September 2021
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference FDAR
Notes DAG Approved no
Call Number Admin @ si @ Serial 3677
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Author Bartlomiej Twardowski; Pawel Zawistowski; Szymon Zaborowski
Title Metric Learning for Session-Based Recommendations Type Conference Article
Year 2021 Publication (up) 43rd edition of the annual BCS-IRSG European Conference on Information Retrieval Abbreviated Journal
Volume 12656 Issue Pages 650-665
Keywords Session-based recommendations; Deep metric learning; Learning to rank
Abstract Session-based recommenders, used for making predictions out of users’ uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study.
Address Virtual; March 2021
Corporate Author Thesis
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECIR
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ TZZ2021 Serial 3586
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Author Jialuo Chen; Mohamed Ali Souibgui; Alicia Fornes; Beata Megyesi
Title Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images Type Conference Article
Year 2021 Publication (up) 4th International Conference on Historical Cryptology Abbreviated Journal
Volume Issue Pages 34-37
Keywords
Abstract Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering.
Address Virtual; September 2021
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Area Expedition Conference HistoCrypt
Notes DAG; 602.230; 600.140; 600.121 Approved no
Call Number Admin @ si @ CSF2021 Serial 3617
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Author Hugo Bertiche; Meysam Madadi; Sergio Escalera
Title PBNS: Physically Based Neural Simulation for Unsupervised Garment Pose Space Deformation Type Journal Article
Year 2021 Publication (up) ACM Transactions on Graphics Abbreviated Journal
Volume 40 Issue 6 Pages 1-14
Keywords
Abstract We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for rigged garments through deep learning. Classical approaches rely on Physically Based Simulations (PBS) to animate clothes. These are general solutions that, given a sufficiently fine-grained discretization of space and time, can achieve highly realistic results. However, they are computationally expensive and any scene modification prompts the need of re-simulation. Linear Blend Skinning (LBS) with PSD offers a lightweight alternative to PBS, though, it needs huge volumes of data to learn proper PSD. We propose using deep learning, formulated as an implicit PBS, to unsupervisedly learn realistic cloth Pose Space Deformations in a constrained scenario: dressed humans. Furthermore, we show it is possible to train these models in an amount of time comparable to a PBS of a few sequences. To the best of our knowledge, we are the first to propose a neural simulator for cloth.
While deep-based approaches in the domain are becoming a trend, these are data-hungry models. Moreover, authors often propose complex formulations to better learn wrinkles from PBS data. Supervised learning leads to physically inconsistent predictions that require collision solving to be used. Also, dependency on PBS data limits the scalability of these solutions, while their formulation hinders its applicability and compatibility. By proposing an unsupervised methodology to learn PSD for LBS models (3D animation standard), we overcome both of these drawbacks. Results obtained show cloth-consistency in the animated garments and meaningful pose-dependant folds and wrinkles. Our solution is extremely efficient, handles multiple layers of cloth, allows unsupervised outfit resizing and can be easily applied to any custom 3D avatar.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ BME2021c Serial 3643
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Author Sonia Baeza; R.Domingo; M.Salcedo; G.Moragas; J.Deportos; I.Garcia Olive; Carles Sanchez; Debora Gil; Antoni Rosell
Title Artificial Intelligence to Optimize Pulmonary Embolism Diagnosis During Covid-19 Pandemic by Perfusion SPECT/CT, a Pilot Study Type Journal Article
Year 2021 Publication (up) American Journal of Respiratory and Critical Care Medicine Abbreviated Journal
Volume Issue Pages
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Address
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Notes IAM; 600.145 Approved no
Call Number Admin @ si @ BDS2021 Serial 3591
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Author Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil
Title Mental Workload Detection Based on EEG Analysis Type Conference Article
Year 2021 Publication (up) Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. Abbreviated Journal
Volume 339 Issue Pages 268-277
Keywords Cognitive states; Mental workload; EEG analysis; Neural Networks.
Abstract The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training.
In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation.
Address Virtual; October 20-22 2021
Corporate Author Thesis
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CCIA
Notes IAM; 600.139; 600.118; 600.145 Approved no
Call Number Admin @ si @ Serial 3723
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Author Pau Riba; Sounak Dey; Ali Furkan Biten; Josep Llados
Title Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild Type Miscellaneous
Year 2021 Publication (up) Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat baseline that without any specific SGOL training is able to outperform the previous works on a fixed set of classes. The baseline is useful to analyze the performance of SGOL approaches based on available simple yet powerful methods. We advance prior arts by proposing a sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a hard classification and alleviates the domain gap between sketches and images to localize object instances. Although the main goal of SGOL is focused on object detection, we explored its natural extension to sketch-guided instance segmentation. This novel task allows to move towards identifying the objects at pixel level, which is of key importance in several applications. We experimentally demonstrate that our model and its variants significantly advance over previous state-of-the-art results. All training and testing code of our model will be released to facilitate future researchhttps://github.com/priba/sgol_wild.
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Notes DAG; 600.121 Approved no
Call Number Admin @ si @ RDB2021 Serial 3674
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Author Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde
Title Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 - ℓ0 layer decomposition domain Type Journal Article
Year 2021 Publication (up) Biomedical Signal Processing and Control Abbreviated Journal BSPC
Volume 68 Issue Pages 102535
Keywords
Abstract Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ DGR2021b Serial 3636
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Author Clementine Decamps; Alexis Arnaud; Florent Petitprez; Mira Ayadi; Aurelia Baures; Lucile Armenoult; Sergio Escalera; Isabelle Guyon; Remy Nicolle; Richard Tomasini; Aurelien de Reynies; Jerome Cros; Yuna Blum; Magali Richard
Title DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification Type Journal Article
Year 2021 Publication (up) BMC Bioinformatics Abbreviated Journal
Volume 22 Issue Pages 473
Keywords
Abstract Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ DAP2021 Serial 3650
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Author Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; Alvaro Pascual; B. Cardenas; G. Fonseka; E. Anton; Richard Frodsham; Francesca Vidal; Zaida Sarrate
Title Chromosomal positioning in spermatogenic cells is influenced by chromosomal factors associated with gene activity, bouquet formation, and meiotic sex-chromosome inactivation Type Journal Article
Year 2021 Publication (up) Chromosoma Abbreviated Journal
Volume 130 Issue Pages 163-175
Keywords
Abstract Chromosome territoriality is not random along the cell cycle and it is mainly governed by intrinsic chromosome factors and gene expression patterns. Conversely, very few studies have explored the factors that determine chromosome territoriality and its influencing factors during meiosis. In this study, we analysed chromosome positioning in murine spermatogenic cells using three-dimensionally fluorescence in situ hybridization-based methodology, which allows the analysis of the entire karyotype. The main objective of the study was to decipher chromosome positioning in a radial axis (all analysed germ-cell nuclei) and longitudinal axis (only spermatozoa) and to identify the chromosomal factors that regulate such an arrangement. Results demonstrated that the radial positioning of chromosomes during spermatogenesis was cell-type specific and influenced by chromosomal factors associated to gene activity. Chromosomes with specific features that enhance transcription (high GC content, high gene density and high numbers of predicted expressed genes) were preferentially observed in the inner part of the nucleus in virtually all cell types. Moreover, the position of the sex chromosomes was influenced by their transcriptional status, from the periphery of the nucleus when its activity was repressed (pachytene) to a more internal position when it is partially activated (spermatid). At pachytene, chromosome positioning was also influenced by chromosome size due to the bouquet formation. Longitudinal chromosome positioning in the sperm nucleus was not random either, suggesting the importance of ordered longitudinal positioning for the release and activation of the paternal genome after fertilisation.
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Area Expedition Conference
Notes IAM; 600.145 Approved no
Call Number Admin @ si @ SBG2021 Serial 3592
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Author Giuseppe Pezzano; Vicent Ribas Ripoll; Petia Radeva
Title CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation Type Journal Article
Year 2021 Publication (up) Computer Methods and Programs in Biomedicine Abbreviated Journal CMPB
Volume 198 Issue Pages 105792
Keywords
Abstract Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of and respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ PRR2021 Serial 3530
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Author Yaxing Wang; Abel Gonzalez-Garcia; Luis Herranz; Joost Van de Weijer
Title Controlling biases and diversity in diverse image-to-image translation Type Journal Article
Year 2021 Publication (up) Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 202 Issue Pages 103082
Keywords
Abstract JCR 2019 Q2, IF=3.121
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes.
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Notes LAMP; 600.141; 600.109; 600.147 Approved no
Call Number Admin @ si @ WGH2021 Serial 3464
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Author Henry Velesaca; Patricia Suarez; Raul Mira; Angel Sappa
Title Computer Vision based Food Grain Classification: a Comprehensive Survey Type Journal Article
Year 2021 Publication (up) Computers and Electronics in Agriculture Abbreviated Journal CEA
Volume 187 Issue Pages 106287
Keywords
Abstract This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches proposed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented.
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Notes MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ VSM2021 Serial 3576
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