| Records |
| Author |
Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal |
| Title |
DocEnTr: An End-to-End Document Image Enhancement Transformer |
Type |
Conference Article |
| Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1699-1705 |
| Keywords |
Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads |
Abstract  |
Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR |
| Address |
August 21-25, 2022 , Montréal Québec |
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ICPR |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ SBJ2022 |
Serial |
3730 |
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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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| Notes |
DAG; 602.230; 600.121; 600.140 |
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no |
| Call Number |
Admin @ si @ SoK2022 |
Serial |
3454 |
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| Author |
Marc Oliu; Sarah Adel Bargal; Stan Sclaroff; Xavier Baro; Sergio Escalera |
| Title |
Multi-varied Cumulative Alignment for Domain Adaptation |
Type |
Conference Article |
| Year |
2022 |
Publication |
6th International Conference on Image Analysis and Processing |
Abbreviated Journal |
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| Volume |
13232 |
Issue |
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Pages |
324–334 |
| Keywords |
Domain Adaptation; Computer vision; Neural networks |
| Abstract |
Domain Adaptation methods can be classified into two basic families of approaches: non-parametric and parametric. Non-parametric approaches depend on statistical indicators such as feature covariances to minimize the domain shift. Non-parametric approaches tend to be fast to compute and require no additional parameters, but they are unable to leverage probability density functions with complex internal structures. Parametric approaches, on the other hand, use models of the probability distributions as surrogates in minimizing the domain shift, but they require additional trainable parameters to model these distributions. In this work, we propose a new statistical approach to minimizing the domain shift based on stochastically projecting and evaluating the cumulative density function in both domains. As with non-parametric approaches, there are no additional trainable parameters. As with parametric approaches, the internal structure of both domains’ probability distributions is considered, thus leveraging a higher amount of information when reducing the domain shift. Evaluation on standard datasets used for Domain Adaptation shows better performance of the proposed model compared to non-parametric approaches while being competitive with parametric ones. (Code available at: https://github.com/moliusimon/mca). |
| Address |
Indonesia; October 2022 |
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ICIAP |
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HuPBA; no menciona |
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no |
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Admin @ si @ OAS2022 |
Serial |
3777 |
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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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ICFHR |
| Notes |
DAG; 600.162; 600.140; 602.230 |
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no |
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Admin @ si @ BRF2022b |
Serial |
3740 |
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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 |
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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 |
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no |
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Admin @ si @ TCC2022 |
Serial |
3743 |
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| Author |
Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier |
| Title |
Modeling, Recognizing, and Explaining Apparent Personality from Videos |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
13 |
Issue |
2 |
Pages |
894-911 |
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| Abstract |
Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future. |
| Address |
1 April-June 2022 |
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| Notes |
HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ EKS2022 |
Serial |
3406 |
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| Author |
Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
| Title |
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning |
Type |
Conference Article |
| Year |
2022 |
Publication |
Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
1381-1390 |
| Keywords |
Measurement; Training; Visualization; Analytical models; Computer vision; Computational modeling; Training data |
| Abstract |
Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient training augmentation method for sentences which requires no new training data or increase
in the model size. By extensive analysis, we show that the proposed methods can significantly diminish our models’ object bias on hallucination metrics. Moreover, we experimentally demonstrate that our methods decrease the dependency on the visual features. All of our code, configuration files and model weights are available online. |
| Address |
Virtual; Waikoloa; Hawai; USA; January 2022 |
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WACV |
| Notes |
DAG; 600.155; 302.105 |
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no |
| Call Number |
Admin @ si @ BGK2022 |
Serial |
3662 |
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| Author |
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 |
| Year |
2022 |
Publication |
IEEE Transactions on Information Forensics and Security |
Abbreviated Journal |
TIForensicSEC |
| Volume |
17 |
Issue |
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Pages |
2497 - 2507 |
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| Abstract |
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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no |
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Admin @ si @ LZY2022 |
Serial |
3778 |
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| Author |
Javier Rodenas; Bhalaji Nagarajan; Marc Bolaños; Petia Radeva |
| Title |
Learning Multi-Subset of Classes for Fine-Grained Food Recognition |
Type |
Conference Article |
| Year |
2022 |
Publication |
7th International Workshop on Multimedia Assisted Dietary Management |
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17–26 |
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Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets. |
| Address |
Lisboa; Portugal; October 2022 |
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MADiMa |
| Notes |
MILAB |
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no |
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Admin @ si @ RNB2022 |
Serial |
3797 |
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| Author |
Marc Masana; Xialei Liu; Bartlomiej Twardowski; Mikel Menta; Andrew Bagdanov; Joost Van de Weijer |
| Title |
Class-incremental learning: survey and performance evaluation |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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For future learning systems incremental learning is desirable, because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning. The main challenge for incremental learning is catastrophic forgetting, which refers to the precipitous drop in performance on previously learned tasks after learning a new one. Incremental learning of deep neural networks has seen explosive growth in recent years. Initial work focused on task incremental learning, where a task-ID is provided at inference time. Recently we have seen a shift towards class-incremental learning where the learner must classify at inference time between all classes seen in previous tasks without recourse to a task-ID. In this paper, we provide a complete survey of existing methods for incremental learning, and in particular we perform an extensive experimental evaluation on twelve class-incremental methods. We consider several new experimental scenarios, including a comparison of class-incremental methods on multiple large-scale datasets, investigation into small and large domain shifts, and comparison on various network architectures. |
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LAMP; 600.120 |
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no |
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Admin @ si @ MLT2022 |
Serial |
3538 |
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Hector Laria Mantecon; Yaxing Wang; Joost Van de Weijer; Bogdan Raducanu |
| Title |
Transferring Unconditional to Conditional GANs With Hyper-Modulation |
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Conference Article |
| Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality pretrained GANs are unconditional (like StyleGAN). For many applications, however, conditional GANs are preferable, because they provide more control over the generation process, despite often suffering more training difficulties. Therefore, in this paper, we focus on transferring from high-quality pretrained unconditional GANs to conditional GANs. This requires architectural adaptation of the pretrained GAN to perform the conditioning. To this end, we propose hyper-modulated generative networks that allow for shared and complementary supervision. To prevent the additional weights of the hypernetwork to overfit, with subsequent mode collapse on small target domains, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the transfer, we apply contrastive learning in the discriminator, which effectively works on very limited batch sizes. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on standard benchmarks. |
| Address |
New Orleans; USA; June 2022 |
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CVPRW |
| Notes |
LAMP; 600.147; 602.200 |
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no |
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LWW2022a |
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3785 |
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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 |
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13804 |
Issue |
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Pages |
329–344 |
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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. |
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LNCS |
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978-3-031-25068-2 |
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ECCV-TiE |
| Notes |
DAG; 600.162; 600.140; 110.312 |
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no |
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Admin @ si @ GBC2022 |
Serial |
3795 |
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| Author |
Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
| Title |
Lost in Transcription of Graphic Signs in Ciphers |
Type |
Conference Article |
| Year |
2022 |
Publication |
International Conference on Historical Cryptology (HistoCrypt 2022) |
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Pages |
153-158 |
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transcription of ciphers; hand-written text recognition of symbols; graphic signs |
| Abstract |
Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. |
| Address |
Amsterdam, Netherlands, June 20-22, 2022 |
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HystoCrypt |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
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no |
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Admin @ si @ MBS2022 |
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3731 |
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| Author |
S.K. Jemni; Mohamed Ali Souibgui; Yousri Kessentini; Alicia Fornes |
| Title |
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement |
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Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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123 |
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Pages |
108370 |
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| Abstract |
Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a and form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task. |
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DAG; 600.124; 600.121; 602.230 |
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Admin @ si @ JSK2022 |
Serial |
3613 |
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| Author |
Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang |
| Title |
Improving Handwritten Music Recognition through Language Model Integration |
Type |
Conference Article |
| Year |
2022 |
Publication |
4th International Workshop on Reading Music Systems (WoRMS2022) |
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42-46 |
| Keywords |
optical music recognition; historical sources; diversity; music theory; digital humanities |
| Abstract |
Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. |
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November 18, 2022 |
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WoRMS |
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DAG; 600.121; 600.162; 602.230 |
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Admin @ si @ TBF2022 |
Serial |
3735 |
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