| Records |
| Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Sabari Nathan; Priya Kansal; Armin Mehri; Parichehr Behjati Ardakani; A.Dalal; A.Akula; D.Sharma; S.Pandey; B.Kumar; J.Yao; R.Wu; KFeng; N.Li; Y.Zhao; H.Patel; V. Chudasama; K.Pjajapati; A.Sarvaiya; K.Upla; K.Raja; R.Ramachandra; C.Bush; F.Almasri; T.Vandamme; O.Debeir; N.Gutierrez; Q.Nguyen; W.Beksi |
| Title |
Thermal Image Super-Resolution Challenge – PBVS 2021 |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
4359-4367 |
| Keywords |
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| Abstract |
This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used; only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image; this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results outperformed those from the first challenge, thus showing an improvement in both evaluation metrics. |
| Address |
Virtual; June 2021 |
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Conference |
CVPRW |
| Notes |
MSIAU; 600.130; 600.122 |
Approved |
no |
| Call Number |
Admin @ si @ RSV2021 |
Serial |
3581 |
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| Author |
Lei Kang; Pau Riba; Marcal Rusinol; Alicia Fornes; Mauricio Villegas |
| Title |
Content and Style Aware Generation of Text-line Images for Handwriting Recognition |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art. |
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| Notes |
DAG; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ KRR2021 |
Serial |
3612 |
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| Author |
Shun Yao; Fei Yang; Yongmei Cheng; Mikhail Mozerov |
| Title |
3D Shapes Local Geometry Codes Learning with SDF |
Type |
Conference Article |
| Year |
2021 |
Publication |
International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2110-2117 |
| Keywords |
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| Abstract |
A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired by the state-of-the-art method DeepSDF [17] that learns and analyzes the 3D shape as the iso-surface of its shell and this method has shown promising results especially in the 3D shape reconstruction and compression domain. In this paper, we consider the degeneration problem of reconstruction coming from the capacity decrease of the DeepSDF model, which approximates the SDF with a neural network and a single latent code. We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape. We add an extra graph neural network to split the single transmittable latent code into a set of local latent codes distributed on the 3D shape. Mentioned latent codes are used to approximate the SDF in their local regions, which will alleviate the complexity of the approximation compared to the original DeepSDF. Furthermore, we introduce a new geometric loss function to facilitate the training of these local latent codes. Note that other local shape adjusting methods use the 3D voxel representation, which in turn is a problem highly difficult to solve or even is insolvable. In contrast, our architecture is based on graph processing implicitly and performs the learning regression process directly in the latent code space, thus make the proposed architecture more flexible and also simple for realization. Our experiments on 3D shape reconstruction demonstrate that our LGCL method can keep more details with a significantly smaller size of the SDF decoder and outperforms considerably the original DeepSDF method under the most important quantitative metrics. |
| Address |
VIRTUAL; October 2021 |
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Conference |
ICCVW |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ YYC2021 |
Serial |
3681 |
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| Author |
Javad Zolfaghari Bengar; Joost Van de Weijer; Bartlomiej Twardowski; Bogdan Raducanu |
| Title |
Reducing Label Effort: Self- Supervised Meets Active Learning |
Type |
Conference Article |
| Year |
2021 |
Publication |
International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1631-1639 |
| Keywords |
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| Abstract |
Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected informative and/or representative samples. Another paradigm to reduce the annotation effort is self-training that learns from a large amount of unlabeled data in an unsupervised way and fine-tunes on few labeled samples. Recent developments in self-training have achieved very impressive results rivaling supervised learning on some datasets. The current work focuses on whether the two paradigms can benefit from each other. We studied object recognition datasets including CIFAR10, CIFAR100 and Tiny ImageNet with several labeling budgets for the evaluations. Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high. The performance gap between active learning trained either with self-training or from scratch diminishes as we approach to the point where almost half of the dataset is labeled. |
| Address |
October 2021 |
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Conference |
ICCVW |
| Notes |
LAMP; |
Approved |
no |
| Call Number |
Admin @ si @ ZVT2021 |
Serial |
3672 |
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| Author |
Hugo Bertiche; Meysam Madadi; Sergio Escalera |
| Title |
Deep Parametric Surfaces for 3D Outfit Reconstruction from Single View Image |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1-8 |
| Keywords |
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| Abstract |
We present a methodology to retrieve analytical surfaces parametrized as a neural network. Previous works on 3D reconstruction yield point clouds, voxelized objects or meshes. Instead, our approach yields 2-manifolds in the euclidean space through deep learning. To this end, we implement a novel formulation for fully connected layers as parametrized manifolds that allows continuous predictions with differential geometry. Based on this property we propose a novel smoothness loss. Results on CLOTH3D++ dataset show the possibility to infer different topologies and the benefits of the smoothness term based on differential geometry. |
| Address |
Virtual; December 2021 |
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FG |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ BME2021 |
Serial |
3640 |
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| Author |
Albert Rial-Farras; Meysam Madadi; Sergio Escalera |
| Title |
UV-based reconstruction of 3D garments from a single RGB image |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1-8 |
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| Abstract |
Garments are highly detailed and dynamic objects made up of particles that interact with each other and with other objects, making the task of 2D to 3D garment reconstruction extremely challenging. Therefore, having a lightweight 3D representation capable of modelling fine details is of great importance. This work presents a deep learning framework based on Generative Adversarial Networks (GANs) to reconstruct 3D garment models from a single RGB image. It has the peculiarity of using UV maps to represent 3D data, a lightweight representation capable of dealing with high-resolution details and wrinkles. With this model and kind of 3D representation, we achieve state-of-the-art results on the CLOTH3D++ dataset, generating good quality and realistic garment reconstructions regardless of the garment topology and shape, human pose, occlusions and lightning. |
| Address |
Virtual; December 2021 |
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FG |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ RME2021 |
Serial |
3639 |
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| Author |
Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío |
| Title |
Matching visual induction effects on screens of different size |
Type |
Journal Article |
| Year |
2021 |
Publication |
Journal of Vision |
Abbreviated Journal |
JOV |
| Volume |
21 |
Issue |
6(10) |
Pages |
1-22 |
| Keywords |
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| Abstract |
In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images. |
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| Notes |
CIC |
Approved |
no |
| Call Number |
Admin @ si @ CVM2021 |
Serial |
3595 |
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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 |
Chromosoma |
Abbreviated Journal |
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| Volume |
130 |
Issue |
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Pages |
163-175 |
| Keywords |
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| 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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| Notes |
IAM; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ SBG2021 |
Serial |
3592 |
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| Author |
Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
| Title |
Beyond Document Object Detection: Instance-Level Segmentation of Complex Layouts |
Type |
Journal Article |
| Year |
2021 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
| Volume |
24 |
Issue |
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Pages |
269–281 |
| Keywords |
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| Abstract |
Information extraction is a fundamental task of many business intelligence services that entail massive document processing. Understanding a document page structure in terms of its layout provides contextual support which is helpful in the semantic interpretation of the document terms. In this paper, inspired by the progress of deep learning methodologies applied to the task of object recognition, we transfer these models to the specific case of document object detection, reformulating the traditional problem of document layout analysis. Moreover, we importantly contribute to prior arts by defining the task of instance segmentation on the document image domain. An instance segmentation paradigm is especially important in complex layouts whose contents should interact for the proper rendering of the page, i.e., the proper text wrapping around an image. Finally, we provide an extensive evaluation, both qualitative and quantitative, that demonstrates the superior performance of the proposed methodology over the current state of the art. |
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| Notes |
DAG; 600.121; 600.140; 110.312 |
Approved |
no |
| Call Number |
Admin @ si @ BRL2021b |
Serial |
3574 |
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| Author |
Minesh Mathew; Lluis Gomez; Dimosthenis Karatzas; C.V. Jawahar |
| Title |
Asking questions on handwritten document collections |
Type |
Journal Article |
| Year |
2021 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
| Volume |
24 |
Issue |
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Pages |
235-249 |
| Keywords |
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| Abstract |
This work addresses the problem of Question Answering (QA) on handwritten document collections. Unlike typical QA and Visual Question Answering (VQA) formulations where the answer is a short text, we aim to locate a document snippet where the answer lies. The proposed approach works without recognizing the text in the documents. We argue that the recognition-free approach is suitable for handwritten documents and historical collections where robust text recognition is often difficult. At the same time, for human users, document image snippets containing answers act as a valid alternative to textual answers. The proposed approach uses an off-the-shelf deep embedding network which can project both textual words and word images into a common sub-space. This embedding bridges the textual and visual domains and helps us retrieve document snippets that potentially answer a question. We evaluate results of the proposed approach on two new datasets: (i) HW-SQuAD: a synthetic, handwritten document image counterpart of SQuAD1.0 dataset and (ii) BenthamQA: a smaller set of QA pairs defined on documents from the popular Bentham manuscripts collection. We also present a thorough analysis of the proposed recognition-free approach compared to a recognition-based approach which uses text recognized from the images using an OCR. Datasets presented in this work are available to download at docvqa.org. |
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| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ MGK2021 |
Serial |
3621 |
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| Author |
AN Ruchai; VI Kober; KA Dorofeev; VN Karnaukhov; Mikhail Mozerov |
| Title |
Classification of breast abnormalities using a deep convolutional neural network and transfer learning |
Type |
Journal Article |
| Year |
2021 |
Publication |
Journal of Communications Technology and Electronics |
Abbreviated Journal |
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| Volume |
66 |
Issue |
6 |
Pages |
778–783 |
| Keywords |
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| Abstract |
A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database. |
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| Notes |
LAMP; |
Approved |
no |
| Call Number |
Admin @ si @ RKD2022 |
Serial |
3680 |
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| Author |
Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus |
| Title |
Mobile eHealth Platform for Home Monitoring of Bipolar Disorder |
Type |
Conference Article |
| Year |
2021 |
Publication |
27th ACM International Conference on Multimedia Modeling |
Abbreviated Journal |
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| Volume |
12573 |
Issue |
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Pages |
330-341 |
| Keywords |
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| Abstract |
People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur.
MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians. |
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LNCS |
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MMM |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CEE2021 |
Serial |
3659 |
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| Author |
Bartlomiej Twardowski; Pawel Zawistowski; Szymon Zaborowski |
| Title |
Metric Learning for Session-Based Recommendations |
Type |
Conference Article |
| Year |
2021 |
Publication |
43rd edition of the annual BCS-IRSG European Conference on Information Retrieval |
Abbreviated Journal |
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| Volume |
12656 |
Issue |
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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 |
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LNCS |
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ECIR |
| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ TZZ2021 |
Serial |
3586 |
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| Author |
Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
| Title |
Graph-Based Deep Generative Modelling for Document Layout Generation |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
12917 |
Issue |
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Pages |
525-537 |
| Keywords |
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| Abstract |
One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices. |
| Address |
Lausanne; Suissa; September 2021 |
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LNCS |
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Edition |
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Conference |
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| Notes |
DAG; 600.121; 600.140; 110.312 |
Approved |
no |
| Call Number |
Admin @ si @ BRL2021 |
Serial |
3676 |
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| Author |
Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
| Title |
A Transcription Is All You Need: Learning to Align through Attention |
Type |
Conference Article |
| Year |
2021 |
Publication |
14th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
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| Volume |
12916 |
Issue |
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Pages |
141–146 |
| Keywords |
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| Abstract |
Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset. |
| Address |
Virtual; September 2021 |
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Thesis |
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Summary Language |
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Original Title |
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Abbreviated Series Title |
LNCS |
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Expedition |
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Conference |
GREC |
| Notes |
DAG; 602.230; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ TSC2021 |
Serial |
3619 |
| Permanent link to this record |
