| Records |
| Author |
Yaxing Wang; Hector Laria Mantecon; Joost Van de Weijer; Laura Lopez-Fuentes; Bogdan Raducanu |
| Title |
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets |
Type  |
Conference Article |
| Year |
2021 |
Publication |
19th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
13990-13999 |
| Keywords |
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| Abstract |
Image-to-image (I2I) translation has matured in recent years and is able to generate high-quality realistic images. However, despite current success, it still faces important challenges when applied to small domains. Existing methods use transfer learning for I2I translation, but they still require the learning of millions of parameters from scratch. This drawback severely limits its application on small domains. In this paper, we propose a new transfer learning for I2I translation (TransferI2I). We decouple our learning process into the image generation step and the I2I translation step. In the first step we propose two novel techniques: source-target initialization and self-initialization of the adaptor layer. The former finetunes the pretrained generative model (e.g., StyleGAN) on source and target data. The latter allows to initialize all non-pretrained network parameters without the need of any data. These techniques provide a better initialization for the I2I translation step. In addition, we introduce an auxiliary GAN that further facilitates the training of deep I2I systems even from small datasets. In extensive experiments on three datasets, (Animal faces, Birds, and Foods), we show that we outperform existing methods and that mFID improves on several datasets with over 25 points. |
| Address |
Virtual; October 2021 |
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Conference |
ICCV |
| Notes |
LAMP; 600.147; 602.200; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ WLW2021 |
Serial |
3604 |
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| Author |
Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui |
| Title |
Generalized Source-free Domain Adaptation |
Type |
Conference Article |
| Year |
2021 |
Publication |
19th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
8958-8967 |
| Keywords |
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| Abstract |
Domain adaptation (DA) aims to transfer the knowledge learned from a source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only a source pre-trained model is available for adaptation to the target domain. However, those methods do not consider keeping source performance which is of high practical value in real world applications. In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation. First, we propose local structure clustering (LSC), aiming to cluster the target features with its semantically similar neighbors, which successfully adapts the model to the target domain in the absence of source data. Second, we propose sparse domain attention (SDA), it produces a binary domain specific attention to activate different feature channels for different domains, meanwhile the domain attention will be utilized to regularize the gradient during adaptation to keep source information. In the experiments, for target performance our method is on par with or better than existing DA and SFDA methods, specifically it achieves state-of-the-art performance (85.4%) on VisDA, and our method works well for all domains after adapting to single or multiple target domains. |
| Address |
Virtual; October 2021 |
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| Notes |
LAMP; 600.120; 600.147 |
Approved |
no |
| Call Number |
Admin @ si @ YWW2021 |
Serial |
3605 |
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| Author |
Hugo Bertiche; Meysam Madadi; Emilio Tylson; Sergio Escalera |
| Title |
DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation |
Type |
Conference Article |
| Year |
2021 |
Publication |
19th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
5471-5480 |
| Keywords |
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| Abstract |
We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. |
| Address |
Virtual; October 2021 |
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Conference |
ICCV |
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BMT2021 |
Serial |
3606 |
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| Author |
Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes |
| Title |
On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition |
Type |
Conference Article |
| Year |
2021 |
Publication |
International Society for Music Information Retrieval Conference |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
690-696 |
| Keywords |
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| Abstract |
Despite the latest advances in Deep Learning, the recognition of handwritten music scores is still a challenging endeavour. Even though the recent Sequence to Sequence(Seq2Seq) architectures have demonstrated its capacity to reliably recognise handwritten text, their performance is still far from satisfactory when applied to historical handwritten scores. Indeed, the ambiguous nature of handwriting, the non-standard musical notation employed by composers of the time and the decaying state of old paper make these scores remarkably difficult to read, sometimes even by trained humans. Thus, in this work we explore the incorporation of language models into a Seq2Seq-based architecture to try to improve transcriptions where the aforementioned unclear writing produces statistically unsound mistakes, which as far as we know, has never been attempted for this field of research on this architecture. After studying various Language Model integration techniques, the experimental evaluation on historical handwritten music scores shows a significant improvement over the state of the art, showing that this is a promising research direction for dealing with such difficult manuscripts. |
| Address |
Virtual; November 2021 |
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Conference |
ISMIR |
| Notes |
DAG; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ TBK2021 |
Serial |
3616 |
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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 |
4th International Conference on Historical Cryptology |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
34-37 |
| Keywords |
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| 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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Place of Publication |
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Summary Language |
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Conference |
HistoCrypt |
| Notes |
DAG; 602.230; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ CSF2021 |
Serial |
3617 |
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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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LNCS |
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Conference |
GREC |
| Notes |
DAG; 602.230; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ TSC2021 |
Serial |
3619 |
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| Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
| Title |
Document Collection Visual Question Answering |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
12822 |
Issue |
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Pages |
778-792 |
| Keywords |
Document collection; Visual Question Answering |
| Abstract |
Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task. |
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LNCS |
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Conference |
ICDAR |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ TKV2021 |
Serial |
3622 |
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| Author |
Ruben Tito; Minesh Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
| Title |
ICDAR 2021 Competition on Document Visual Question Answering |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
635-649 |
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| Abstract |
In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5, 000 infographics images and 30, 000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the submitted methods and the results and analysis of their performance. A summary of the progress made on Single Document VQA since the first edition of the DocVQA 2020 challenge is also presented. |
| Address |
VIRTUAL; Lausanne; Suissa; September 2021 |
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Conference |
ICDAR |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ TMJ2021 |
Serial |
3624 |
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| Author |
Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
| Title |
Modeling long-term interactions to enhance action recognition |
Type |
Conference Article |
| Year |
2021 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
10351-10358 |
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| Abstract |
In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical LongShort-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks,without relying on motion information |
| Address |
January 2021 |
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Conference |
ICPR |
| Notes |
MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ CRD2021 |
Serial |
3626 |
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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 |
34th International Symposium on Computer-Based Medical Systems |
Abbreviated Journal |
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Issue |
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Pages |
492-497 |
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| 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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Conference |
CBMS |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ SBZ2021 |
Serial |
3629 |
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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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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 |
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 |
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Issue |
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Pages |
1-8 |
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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 |
Hugo Bertiche; Meysam Madadi; Sergio Escalera |
| Title |
PBNS: Physically Based Neural Simulation for Unsupervised Garment Pose Space Deformation |
Type |
Conference Article |
| Year |
2021 |
Publication |
14th ACM Siggraph Conference and exhibition on Computer Graphics and Interactive Techniques in Asia |
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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. |
| Address |
Virtual; December 2020 |
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SIGGRAPH |
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HUPBA; no proj |
Approved |
no |
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Admin @ si @ BME2021b |
Serial |
3641 |
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| Author |
Javier M. Olaso; Alain Vazquez; Leila Ben Letaifa; Mikel de Velasco; Aymen Mtibaa; Mohamed Amine Hmani; Dijana Petrovska-Delacretaz; Gerard Chollet; Cesar Montenegro; Asier Lopez-Zorrilla; Raquel Justo; Roberto Santana; Jofre Tenorio-Laranga; Eduardo Gonzalez-Fraile; Begoña Fernandez-Ruanova; Gennaro Cordasco; Anna Esposito; Kristin Beck Gjellesvik; Anna Torp Johansen; Maria Stylianou Kornes; Colin Pickard; Cornelius Glackin; Gary Cahalane; Pau Buch; Cristina Palmero; Sergio Escalera; Olga Gordeeva; Olivier Deroo; Anaïs Fernandez; Daria Kyslitska; Jose Antonio Lozano; Maria Ines Torres; Stephan Schlogl |
| Title |
The EMPATHIC Virtual Coach: a demo |
Type |
Conference Article |
| Year |
2021 |
Publication |
23rd ACM International Conference on Multimodal Interaction |
Abbreviated Journal |
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Issue |
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Pages |
848-851 |
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| Abstract |
The main objective of the EMPATHIC project has been the design and development of a virtual coach to engage the healthy-senior user and to enhance well-being through awareness of personal status. The EMPATHIC approach addresses this objective through multimodal interactions supported by the GROW coaching model. The paper summarizes the main components of the EMPATHIC Virtual Coach (EMPATHIC-VC) and introduces a demonstration of the coaching sessions in selected scenarios. |
| Address |
Virtual; October 2021 |
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ICMI |
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HUPBA; no proj |
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no |
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Admin @ si @ OVB2021 |
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3644 |
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| Author |
Reza Azad; Afshin Bozorgpour; Maryam Asadi-Aghbolaghi; Dorit Merhof; Sergio Escalera |
| Title |
Deep Frequency Re-Calibration U-Net for Medical Image Segmentation |
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Conference Article |
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2021 |
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IEEE/CVF International Conference on Computer Vision Workshops |
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3274-3283 |
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We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. |
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VIRTUAL; October 2021 |
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ICCVW |
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HUPBA; no proj |
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no |
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Admin @ si @ ABA2021 |
Serial |
3645 |
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