| Records |
| Author |
Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas |
| Title |
Out-of-Vocabulary Challenge Report |
Type |
Conference Article |
| Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
13804 |
Issue |
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Pages |
359–375 |
| Keywords |
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| Abstract |
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. |
| Address |
Tel-Aviv; Israel; October 2022 |
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LNCS |
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Conference |
ECCVW |
Notes  |
DAG; 600.155; 302.105; 611.002 |
Approved |
no |
| Call Number |
Admin @ si @ GMB2022 |
Serial |
3771 |
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| Author |
Sergi Garcia Bordils; George Tom; Sangeeth Reddy; Minesh Mathew; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas |
| Title |
Read While You Drive-Multilingual Text Tracking on the Road |
Type |
Conference Article |
| Year |
2022 |
Publication |
15th IAPR International workshop on document analysis systems |
Abbreviated Journal |
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| Volume |
13237 |
Issue |
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Pages |
756–770 |
| Keywords |
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| Abstract |
Visual data obtained during driving scenarios usually contain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully annotated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods exploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild. |
| Address |
La Rochelle; France; May 2022 |
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LNCS |
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Edition |
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ISBN |
978-3-031-06554-5 |
Medium |
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Expedition |
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Conference |
DAS |
| Notes |
DAG; 600.155; 611.022; 611.004 |
Approved |
no |
| Call Number |
Admin @ si @ GTR2022 |
Serial |
3783 |
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| Author |
Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados |
| Title |
Table detection in business document images by message passing networks |
Type |
Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
127 |
Issue |
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Pages |
108641 |
| Keywords |
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| Abstract |
Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches. |
| Address |
July 2022 |
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| Publisher |
Elsevier |
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Conference |
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| Notes |
DAG; 600.162; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ RGR2022 |
Serial |
3729 |
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| Author |
Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai |
| Title |
Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks |
Type |
Conference Article |
| Year |
2022 |
Publication |
17th European Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
13804 |
Issue |
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Pages |
329–344 |
| Keywords |
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| Abstract |
Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection. |
| Address |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Issue |
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Edition |
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ISBN |
978-3-031-25068-2 |
Medium |
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Expedition |
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Conference |
ECCV-TiE |
| Notes |
DAG; 600.162; 600.140; 110.312 |
Approved |
no |
| Call Number |
Admin @ si @ GBC2022 |
Serial |
3795 |
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| Author |
Arnau Baro; Pau Riba; Alicia Fornes |
| Title |
Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network |
Type |
Conference Article |
| Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
Abbreviated Journal |
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| Volume |
13639 |
Issue |
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Pages |
171-184 |
| Keywords |
Object detection; Optical music recognition; Graph neural network |
| Abstract |
During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. |
| Address |
December 04 – 07, 2022; Hyderabad, India |
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LNCS |
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Conference |
ICFHR |
| Notes |
DAG; 600.162; 600.140; 602.230 |
Approved |
no |
| Call Number |
Admin @ si @ BRF2022b |
Serial |
3740 |
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| Author |
Mohamed Ali Souibgui; Y.Kessentini |
| Title |
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
44 |
Issue |
3 |
Pages |
1180-1191 |
| Keywords |
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| Abstract |
Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems. |
| Address |
1 March 2022 |
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Expedition |
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Conference |
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| Notes |
DAG; 602.230; 600.121; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ SoK2022 |
Serial |
3454 |
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| Author |
Mohamed Ali Souibgui; Ali Furkan Biten; Sounak Dey; Alicia Fornes; Yousri Kessentini; Lluis Gomez; Dimosthenis Karatzas; Josep Llados |
| Title |
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition |
Type |
Conference Article |
| Year |
2022 |
Publication |
Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
Document Analysis |
| Abstract |
Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). This appears, for example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the content. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method, achieving competitive results compared to the usage of real annotated data. |
| Address |
Virtual; January 2022 |
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Conference |
WACV |
| Notes |
DAG; 602.230; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ SBD2022 |
Serial |
3615 |
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| Author |
Josep Brugues Pujolras; Lluis Gomez; Dimosthenis Karatzas |
| Title |
A Multilingual Approach to Scene Text Visual Question Answering |
Type |
Conference Article |
| Year |
2022 |
Publication |
Document Analysis Systems.15th IAPR International Workshop, (DAS2022) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
65-79 |
| Keywords |
Scene text; Visual question answering; Multilingual word embeddings; Vision and language; Deep learning |
| Abstract |
Scene Text Visual Question Answering (ST-VQA) has recently emerged as a hot research topic in Computer Vision. Current ST-VQA models have a big potential for many types of applications but lack the ability to perform well on more than one language at a time due to the lack of multilingual data, as well as the use of monolingual word embeddings for training. In this work, we explore the possibility to obtain bilingual and multilingual VQA models. In that regard, we use an already established VQA model that uses monolingual word embeddings as part of its pipeline and substitute them by FastText and BPEmb multilingual word embeddings that have been aligned to English. Our experiments demonstrate that it is possible to obtain bilingual and multilingual VQA models with a minimal loss in performance in languages not used during training, as well as a multilingual model trained in multiple languages that match the performance of the respective monolingual baselines. |
| Address |
La Rochelle, France; May 22–25, 2022 |
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Conference |
DAS |
| Notes |
DAG; 611.004; 600.155; 601.002 |
Approved |
no |
| Call Number |
Admin @ si @ BGK2022b |
Serial |
3695 |
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| Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
| Title |
A Flexible Outlier Detector Based on a Topology Given by Graph Communities |
Type |
Journal Article |
| Year |
2022 |
Publication |
Big Data Research |
Abbreviated Journal |
BDR |
| Volume |
29 |
Issue |
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Pages |
100332 |
| Keywords |
Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors |
| Abstract |
Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings. |
| Address |
August 28, 2022 |
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| Notes |
DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 |
Approved |
no |
| Call Number |
Admin @ si @ RBG2022a |
Serial |
3718 |
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| Author |
Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas |
| Title |
OCR-IDL: OCR Annotations for Industry Document Library Dataset |
Type |
Conference Article |
| Year |
2022 |
Publication |
ECCV Workshop on Text in Everything |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL. |
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ECCV |
| Notes |
DAG; no proj |
Approved |
no |
| Call Number |
Admin @ si @ BTG2022 |
Serial |
3817 |
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| Author |
Julio C. S. Jacques Junior; Yagmur Gucluturk; Marc Perez; Umut Guçlu; Carlos Andujar; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Marcel A. J. van Gerven; Rob van Lier; Sergio Escalera |
| Title |
First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
13 |
Issue |
1 |
Pages |
75-95 |
| Keywords |
Personality computing; first impressions; person perception; big-five; subjective bias; computer vision; machine learning; nonverbal signals; facial expression; gesture; speech analysis; multi-modal recognition |
| Abstract |
Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed. |
| Address |
1 Jan.-March 2022 |
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| Notes |
HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ JGP2022 |
Serial |
3724 |
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| Author |
Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera |
| Title |
Deep learning with self-supervision and uncertainty regularization to count fish in underwater images |
Type |
Journal Article |
| Year |
2022 |
Publication |
PloS One |
Abbreviated Journal |
Plos |
| Volume |
17 |
Issue |
5 |
Pages |
e0267759 |
| Keywords |
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| Abstract |
Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data. |
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Public Library of Science |
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HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ TCC2022 |
Serial |
3743 |
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| Author |
Victor M. Campello; Carlos Martin-Isla; Cristian Izquierdo; Andrea Guala; Jose F. Rodriguez Palomares; David Vilades; Martin L. Descalzo; Mahir Karakas; Ersin Cavus; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Karim Lekadir |
| Title |
Minimising multi-centre radiomics variability through image normalisation: a pilot study |
Type |
Journal Article |
| Year |
2022 |
Publication |
Scientific Reports |
Abbreviated Journal |
ScR |
| Volume |
12 |
Issue |
1 |
Pages |
12532 |
| Keywords |
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| Abstract |
Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification. |
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2022/07/22 |
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Springer Nature |
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HuPBA |
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Admin @ si @ CMI2022 |
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3749 |
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| Author |
Zhen Xu; Sergio Escalera; Adrien Pavao; Magali Richard; Wei-Wei Tu; Quanming Yao; Huan Zhao; Isabelle Guyon |
| Title |
Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform |
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Journal Article |
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2022 |
Publication |
Patterns |
Abbreviated Journal |
PATTERNS |
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3 |
Issue |
7 |
Pages |
100543 |
| Keywords |
Machine learning; data science; benchmark platform; reproducibility; competitions |
| Abstract |
Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning. |
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June 24, 2022 |
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Science Direct |
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Admin @ si @ XEP2022 |
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3764 |
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Ajian Liu; Chenxu Zhao; Zitong Yu; Jun Wan; Anyang Su; Xing Liu; Zichang Tan; Sergio Escalera; Junliang Xing; Yanyan Liang; Guodong Guo; Zhen Lei; Stan Z. Li; Shenshen Du |
| Title |
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection |
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Journal Article |
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2022 |
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IEEE Transactions on Information Forensics and Security |
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TIForensicSEC |
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17 |
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2497 - 2507 |
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Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask identities, types of sensors, and a total number of videos; 2) low-fidelity quality of facial masks. Basic deep models and remote photoplethysmography (rPPG) methods achieved acceptable performance on these benchmarks but still far from the needs of practical scenarios. To bridge the gap to real-world applications, we introduce a large-scale Hi gh- Fi delity Mask dataset, namely HiFiMask . Specifically, a total amount of 54,600 videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of sensors. Along with the dataset, we propose a novel C ontrastive C ontext-aware L earning (CCL) framework. CCL is a new training methodology for supervised PAD tasks, which is able to learn by leveraging rich contexts accurately (e.g., subjects, mask material and lighting) among pairs of live faces and high-fidelity mask attacks. Extensive experimental evaluations on HiFiMask and three additional 3D mask datasets demonstrate the effectiveness of our method. The codes and dataset will be released soon. |
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IEEE |
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HuPBA |
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Admin @ si @ LZY2022 |
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3778 |
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