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Author | Hugo Bertiche; Meysam Madadi; Sergio Escalera | ||||
Title | CLOTH3D: Clothed 3D Humans | Type | Conference Article | ||
Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
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Abstract | This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape. | ||||
Address | Virtual; August 2020 | ||||
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Area | Expedition | Conference | ECCV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ BME2020 | Serial | 3519 | ||
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Author | Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
Title | Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation | Type | Conference Article | ||
Year | 2020 | Publication | Bioimage computation workshop | Abbreviated Journal | |
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Address | Virtual; August 2020 | ||||
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Area | Expedition | Conference | ECCVW | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ AAF2020 | Serial | 3520 | ||
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Author | Petia Radeva | ||||
Title | Uncertainty Modeling within an End-to-end Framework for Food Image Analysis | Type | Conference Article | ||
Year | 2020 | Publication | 1st DELTA | Abbreviated Journal | |
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Area | Expedition | Conference | DELTA | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ Rad2020 | Serial | 3527 | ||
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Author | Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva | ||||
Title | Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams | Type | Conference Article | ||
Year | 2020 | Publication | ECCV Workshops | Abbreviated Journal | |
Volume | 12538 | Issue | Pages | 469-484 | |
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Abstract | The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle. | ||||
Address | Virtual; August 2020 | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCVW | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ MTM2020 | Serial | 3528 | ||
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Author | Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro | ||||
Title | Automatic Reminiscence Therapy for Dementia | Type | Conference Article | ||
Year | 2020 | Publication | 10th ACM International Conference on Multimedia Retrieval | Abbreviated Journal | |
Volume | Issue | Pages | 383-387 | ||
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Abstract | With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. | ||||
Address | Virtual; October 2020 | ||||
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Area | Expedition | Conference | ICRM | ||
Notes | Approved | no | |||
Call Number | Admin @ si @ CGR2020 | Serial | 3529 | ||
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Author | Esmitt Ramirez; Carles Sanchez; Debora Gil | ||||
Title | Localizing Pulmonary Lesions Using Fuzzy Deep Learning | Type | Conference Article | ||
Year | 2019 | Publication | 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing | Abbreviated Journal | |
Volume | Issue | Pages | 290-294 | ||
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Abstract | The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions. | ||||
Address | Timisoara; Rumania; September 2019 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SYNASC | ||
Notes | IAM; 600.145; 600.140; 601.337; 601.323 | Approved | no | ||
Call Number | Admin @ si @ RSG2019 | Serial | 3531 | ||
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Author | Cristina Palmero; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; Albert Clapes; Alexa Mosegui; Zejian Zhang; David Gallardo; Georgina Guilera; David Leiva; Sergio Escalera | ||||
Title | Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1-12 | ||
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Abstract | This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a
transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to regress a target person’s personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information. |
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Address | Virtual; January 2021 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ PSS2021 | Serial | 3532 | ||
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Author | Julio C. S. Jacques Junior; Agata Lapedriza; Cristina Palmero; Xavier Baro; Sergio Escalera | ||||
Title | Person Perception Biases Exposed: Revisiting the First Impressions Dataset | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 13-21 | ||
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Abstract | This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the original pairwise annotations, and reveal existing person perception biases associated to perceived attributes like gender, ethnicity, age and face attractiveness.
We show how person perception bias can influence data labelling of a subjective task, which has received little attention from the computer vision and machine learning communities by now. We further show that the mechanism used to convert pairwise annotations to continuous values may magnify the biases if no special treatment is considered. The findings of this study are relevant for the computer vision community that is still creating new datasets on subjective tasks, and using them for practical applications, ignoring these perceptual biases. |
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Address | Virtual; January 2021 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ JLP2021 | Serial | 3533 | ||
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Author | Carola Figueroa Flores; Bogdan Raducanu; David Berga; Joost Van de Weijer | ||||
Title | Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
Volume | 4 | Issue | Pages | 163-171 | |
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Abstract | arXiv:2007.12562
Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification. In the current paper, we propose an approach which does not require explicit saliency maps to improve image classification, but they are learned implicitely, during the training of an end-to-end image classification task. We show that our approach obtains similar results as the case when the saliency maps are provided explicitely. Combining RGB data with saliency maps represents a significant advantage for object recognition, especially for the case when training data is limited. We validate our method on several datasets for fine-grained classification tasks (Flowers, Birds and Cars). In addition, we show that our saliency estimation method, which is trained without any saliency groundtruth data, obtains competitive results on real image saliency benchmark (Toronto), and outperforms deep saliency models with synthetic images (SID4VAM). |
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Address | Virtual; February 2021 | ||||
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Area | Expedition | Conference | VISAPP | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ FRB2021c | Serial | 3540 | ||
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Author | Kai Wang; Luis Herranz; Joost Van de Weijer | ||||
Title | Continual learning in cross-modal retrieval | Type | Conference Article | ||
Year | 2021 | Publication | 2nd CLVISION workshop | Abbreviated Journal | |
Volume | Issue | Pages | 3628-3638 | ||
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Abstract | Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline. | ||||
Address | Virtual; June 2021 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | LAMP; 600.120; 600.141; 600.147; 601.379 | Approved | no | ||
Call Number | Admin @ si @ WHW2021 | Serial | 3566 | ||
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Author | Vincenzo Lomonaco; Lorenzo Pellegrini; Andrea Cossu; Antonio Carta; Gabriele Graffieti; Tyler L. Hayes; Matthias De Lange; Marc Masana; Jary Pomponi; Gido van de Ven; Martin Mundt; Qi She; Keiland Cooper; Jeremy Forest; Eden Belouadah; Simone Calderara; German I. Parisi; Fabio Cuzzolin; Andreas Tolias; Simone Scardapane; Luca Antiga; Subutai Amhad; Adrian Popescu; Christopher Kanan; Joost Van de Weijer; Tinne Tuytelaars; Davide Bacciu; Davide Maltoni | ||||
Title | Avalanche: an End-to-End Library for Continual Learning | Type | Conference Article | ||
Year | 2021 | Publication | 34th IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 3595-3605 | ||
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Abstract | Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms. | ||||
Address | Virtual; June 2021 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ LPC2021 | Serial | 3567 | ||
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Author | Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat | ||||
Title | Weakly Supervised Multi-Object Tracking and Segmentation | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 125-133 | ||
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Abstract | We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
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Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | WACVW | ||
Notes | ADAS; 600.118; 600.124 | Approved | no | ||
Call Number | Admin @ si @ RPR2021 | Serial | 3548 | ||
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Author | Idoia Ruiz; Joan Serrat | ||||
Title | Rank-based ordinal classification | Type | Conference Article | ||
Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 8069-8076 | ||
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Abstract | Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1. . . C, being C the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to least likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing
that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset. |
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Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | ADAS; 600.118; 600.124 | Approved | no | ||
Call Number | Admin @ si @ RuS2020 | Serial | 3549 | ||
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Author | Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca | ||||
Title | Towards Visual Personality Questionnaires based on Deep Learning and Social Media | Type | Conference Article | ||
Year | 2019 | Publication | 21st International Conference on Social Influence and Social Psychology | Abbreviated Journal | |
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Address | April 2019; Tokio; Japan | ||||
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Area | Expedition | Conference | ICSISP | ||
Notes | ISE; 600.119 | Approved | no | ||
Call Number | Admin @ si @ RGG2020 | Serial | 3554 | ||
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Author | Klara Janousckova; Jiri Matas; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Text Recognition – Real World Data and Where to Find Them | Type | Conference Article | ||
Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 4489-4496 | ||
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Abstract | We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets 1 1 Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya. | ||||
Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ JMG2020 | Serial | 3557 | ||
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