| Records |
| 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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| 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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Conference |
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| Notes |
DAG; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ KRR2021 |
Serial |
3612 |
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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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Conference |
HistoCrypt |
| Notes |
DAG; 602.230; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ CSF2021 |
Serial |
3617 |
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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 |
| Keywords |
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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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| Volume |
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Issue |
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Pages |
10351-10358 |
| Keywords |
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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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| Volume |
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Issue |
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Pages |
492-497 |
| Keywords |
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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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| Volume |
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Issue |
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Pages |
1-8 |
| Keywords |
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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 |
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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Conference |
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 |
Abbreviated Journal |
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Issue |
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Pages |
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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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Conference |
SIGGRAPH |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
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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| Volume |
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Issue |
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Pages |
848-851 |
| Keywords |
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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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Conference |
ICMI |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ OVB2021 |
Serial |
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 |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3274-3283 |
| 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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ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ABA2021 |
Serial |
3645 |
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| Author |
Ajian Liu; Chenxu Zhao; Zitong Yu; Anyang Su; Xing Liu; Zijian Kong; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei; Guodong Guo |
| Title |
3D High-Fidelity Mask Face Presentation Attack Detection Challenge |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
814-823 |
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| Abstract |
The threat of 3D mask to face recognition systems is increasing serious, and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of total amount of 54,600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask based attack detection. It attracted more than 200 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranked algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition. |
| Address |
Virtual; October 2021 |
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Conference |
ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LZY2021 |
Serial |
3646 |
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| Author |
Claudia Greco; Carmela Buono; Pau Buch-Cardona; Gennaro Cordasco; Sergio Escalera; Anna Esposito; Anais Fernandez; Daria Kyslitska; Maria Stylianou Kornes; Cristina Palmero; Jofre Tenorio Laranga; Anna Torp Johansen; Maria Ines Torres |
| Title |
Emotional Features of Interactions With Empathic Agents |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2168-2176 |
| Keywords |
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| Abstract |
The current study is part of the EMPATHIC project, whose aim is to develop an Empathic Virtual Coach (VC) capable of promoting healthy and independent aging. To this end, the VC needs to be capable of perceiving the emotional states of users and adjusting its behaviour during the interactions according to what the users are experiencing in terms of emotions and comfort. Thus, the present work focuses on some sessions where elderly users of three different countries interact with a simulated system. Audio and video information extracted from these sessions were examined by external observers to assess participants' emotional experience with the EMPATHIC-VC in terms of categorical and dimensional assessment of emotions. Analyses were conducted on the emotional labels assigned by the external observers while participants were engaged in two different scenarios: a generic one, where the interaction was carried out with no intention to discuss a specific topic, and a nutrition one, aimed to accomplish a conversation on users' nutritional habits. Results of analyses performed on both audio and video data revealed that the EMPATHIC coach did not elicit negative feelings in the users. Indeed, users from all countries have shown relaxed and positive behavior when interacting with the simulated VC during both scenarios. Overall, the EMPATHIC-VC was capable to offer an enjoyable experience without eliciting negative feelings in the users. This supports the hypothesis that an Empathic Virtual Coach capable of considering users' expectations and emotional states could support elderly people in daily life activities and help them to remain independent. |
| Address |
VIRTUAL; October 2021 |
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ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ GBB2021 |
Serial |
3647 |
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| Author |
David Curto; Albert Clapes; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; David Gallardo-Pujol; Georgina Guilera; David Leiva; Thomas B. Moeslund; Sergio Escalera; Cristina Palmero |
| Title |
Dyadformer: A Multi-Modal Transformer for Long-Range Modeling of Dyadic Interactions |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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2177-2188 |
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Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for. However, most works on the topic have focused on analyzing the individual, even when applied to interaction scenarios, and for short periods of time. To address these limitations, we present the Dyadformer, a novel multi-modal multi-subject Transformer architecture to model individual and interpersonal features in dyadic interactions using variable time windows, thus allowing the capture of long-term interdependencies. Our proposed cross-subject layer allows the network to explicitly model interactions among subjects through attentional operations. This proof-of-concept approach shows how multi-modality and joint modeling of both interactants for longer periods of time helps to predict individual attributes. With Dyadformer, we improve state-of-the-art self-reported personality inference results on individual subjects on the UDIVA v0.5 dataset. |
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Virtual; October 2021 |
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HUPBA; no proj |
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no |
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Admin @ si @ CCS2021 |
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3648 |
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| Author |
Neelu Madan; Arya Farkhondeh; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund |
| Title |
Temporal Cues From Socially Unacceptable Trajectories for Anomaly Detection |
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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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2150-2158 |
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State-of-the-Art (SoTA) deep learning-based approaches to detect anomalies in surveillance videos utilize limited temporal information, including basic information from motion, e.g., optical flow computed between consecutive frames. In this paper, we compliment the SoTA methods by including long-range dependencies from trajectories for anomaly detection. To achieve that, we first created trajectories by running a tracker on two SoTA datasets, namely Avenue and Shanghai-Tech. We propose a prediction-based anomaly detection method using trajectories based on Social GANs, also called in this paper as temporal-based anomaly detection. Then, we hypothesize that late fusion of the result of this temporal-based anomaly detection system with spatial-based anomaly detection systems produces SoTA results. We verify this hypothesis on two spatial-based anomaly detection systems. We show that both cases produce results better than baseline spatial-based systems, indicating the usefulness of the temporal information coming from the trajectories for anomaly detection. We observe that the proposed approach depicts the maximum improvement in micro-level Area-Under-the-Curve (AUC) by 4.1% on CUHK Avenue and 3.4% on Shanghai-Tech over one of the baseline method. We also show a high performance on cross-data evaluation, where we learn the weights to combine spatial and temporal information on Shanghai-Tech and perform evaluation on CUHK Avenue and vice-versa. |
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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 @ MFN2021 |
Serial |
3649 |
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