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Author Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas
Title GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images Type Conference Article
Year 2020 Publication 16th European Conference on Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images.
Address (down) Virtual; August 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCV
Notes DAG; 600.140; 600.121; 600.129 Approved no
Call Number Admin @ si @ KPW2020 Serial 3426
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Author Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer
Title Bookworm continual learning: beyond zero-shot learning and continual learning Type Conference Article
Year 2020 Publication Workshop TASK-CV 2020 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem.
Address (down) Virtual; August 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCVW
Notes LAMP; 600.141; 600.120 Approved no
Call Number Admin @ si @ WHD2020 Serial 3466
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Author Tomas Sixta; Julio C. S. Jacques Junior; Pau Buch Cardona; Eduard Vazquez; Sergio Escalera
Title FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition Type Conference Article
Year 2020 Publication ECCV Workshops Abbreviated Journal
Volume 12540 Issue Pages 463-481
Keywords
Abstract This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too.
Address (down) Virtual; August 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCVW
Notes HUPBA Approved no
Call Number Admin @ si @ SJB2020 Serial 3499
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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
Volume Issue Pages
Keywords
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 (down) Virtual; August 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference 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
Volume Issue Pages
Keywords
Abstract
Address (down) Virtual; August 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCVW
Notes HUPBA Approved no
Call Number Admin @ si @ AAF2020 Serial 3520
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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
Keywords
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 (down) Virtual; August 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
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 Zhaocheng Liu; Luis Herranz; Fei Yang; Saiping Zhang; Shuai Wan; Marta Mrak; Marc Gorriz
Title Slimmable Video Codec Type Conference Article
Year 2022 Publication CVPR 2022 Workshop and Challenge on Learned Image Compression (CLIC 2022, 5th Edition) Abbreviated Journal
Volume Issue Pages 1742-1746
Keywords
Abstract Neural video compression has emerged as a novel paradigm combining trainable multilayer neural net-works and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeoff. Recent slimmable image codecs can dynamically adjust their model capacity to gracefully reduce the memory and computation requirements, without harming RD performance. In this paper we propose a slimmable video codec (SlimVC), by integrating a slimmable temporal entropy model in a slimmable autoencoder. Despite a significantly more complex architecture, we show that slimming remains a powerful mechanism to control rate, memory footprint, computational cost and latency, all being important requirements for practical video compression.
Address (down) Virtual; 19 June 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes MACO; 601.379; 601.161 Approved no
Call Number Admin @ si @ LHY2022 Serial 3687
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Author Henry Velesaca; Steven Araujo; Patricia Suarez; Angel Sanchez; Angel Sappa
Title Off-the-Shelf Based System for Urban Environment Video Analytics Type Conference Article
Year 2020 Publication 27th International Conference on Systems, Signals and Image Processing Abbreviated Journal
Volume Issue Pages
Keywords greenhouse gases; carbon footprint; object detection; object tracking; website framework; off-the-shelf video analytics
Abstract This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to
public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.
Address (down) Virtual IWSSIP
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IWSSIP
Notes MSIAU; 600.130; 601.349; 600.122 Approved no
Call Number Admin @ si @ VAS2020 Serial 3429
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Author Arnau Baro; Alicia Fornes; Carles Badal
Title Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism Type Conference Article
Year 2020 Publication 17th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks.
Address (down) Virtual ICFHR; September 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICFHR
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ BFB2020 Serial 3448
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Author Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas
Title Distilling Content from Style for Handwritten Word Recognition Type Conference Article
Year 2020 Publication 17th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten
word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes.
Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both
the generative process and the recognition efficiency in the IAM dataset.
Address (down) Virtual ICFHR; September 2020
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICFHR
Notes DAG; 600.129; 600.140; 600.121 Approved no
Call Number Admin @ si @ KRR2020 Serial 3425
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Author Xialei Liu; Chenshen Wu; Mikel Menta; Luis Herranz; Bogdan Raducanu; Andrew Bagdanov; Shangling Jui; Joost Van de Weijer
Title Generative Feature Replay for Class-Incremental Learning Type Conference Article
Year 2020 Publication CLVISION – Workshop on Continual Learning in Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. The imbalance between old and new classes typically results in a bias of the network towards the newest ones. This imbalance problem can either be addressed by storing exemplars from previous tasks, or by using image replay methods. However, the latter can only be applied to toy datasets since image generation for complex datasets is a hard problem.
We propose a solution to the imbalance problem based on generative feature replay which does not require any exemplars. To do this, we split the network into two parts: a feature extractor and a classifier. To prevent forgetting, we combine generative feature replay in the classifier with feature distillation in the feature extractor. Through feature generation, our method reduces the complexity of generative replay and prevents the imbalance problem. Our approach is computationally efficient and scalable to large datasets. Experiments confirm that our approach achieves state-of-the-art results on CIFAR-100 and ImageNet, while requiring only a fraction of the storage needed for exemplar-based continual learning
Address (down) Virtual CVPR
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes LAMP; 601.309; 602.200; 600.141; 600.120 Approved no
Call Number Admin @ si @ LWM2020 Serial 3419
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Author Yaxing Wang; Abel Gonzalez-Garcia; David Berga; Luis Herranz; Fahad Shahbaz Khan; Joost Van de Weijer
Title MineGAN: effective knowledge transfer from GANs to target domains with few images Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs.
Address (down) Virtual CVPR
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes LAMP; 600.109; 600.141; 600.120 Approved no
Call Number Admin @ si @ WGB2020 Serial 3421
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Author Lu Yu; Bartlomiej Twardowski; Xialei Liu; Luis Herranz; Kai Wang; Yongmai Cheng; Shangling Jui; Joost Van de Weijer
Title Semantic Drift Compensation for Class-Incremental Learning of Embeddings Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers to the drastic drop in performance on previous tasks. The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes. Embedding networks have the advantage that new classes can be naturally included into the network without adding new weights. Therefore, we study incremental learning for embedding networks. In addition, we propose a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars. We approximate the drift of previous tasks based on the drift that is experienced by current task data. We perform experiments on fine-grained datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks suffer significantly less from catastrophic forgetting. We outperform existing methods which do not require exemplars and obtain competitive results compared to methods which store exemplars. Furthermore, we show that our proposed SDC when combined with existing methods to prevent forgetting consistently improves results.
Address (down) Virtual CVPR
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes LAMP; 600.141; 601.309; 602.200; 600.120 Approved no
Call Number Admin @ si @ YTL2020 Serial 3422
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Author Henry Velesaca; Raul Mira; Patricia Suarez; Christian X. Larrea; Angel Sappa
Title Deep Learning Based Corn Kernel Classification Type Conference Article
Year 2020 Publication 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learningbased approach, the Mask R-CNN architecture, while the classification is performed hrough a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been
performed and comparisons with other approaches are provided showing improvements with the proposed pipeline.
Address (down) Virtual CVPR
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ VMS2020 Serial 3430
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Author Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati Ardakani; Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi
Title Thermal Image Super-Resolution Challenge – PBVS 2020 Type Conference Article
Year 2020 Publication 16h IEEE Workshop on Perception Beyond the Visible Spectrum Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state-of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high-resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.
Address (down) Virtual CVPR
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes MSIAU; ISE; 600.119; 600.122 Approved no
Call Number Admin @ si @ RSV2020 Serial 3431
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