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Author |
Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas |
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Title |
Historical review of scene text detection research |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Springer |
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K. Alahari; C.V. Jawahar |
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Series on Advances in Computer Vision and Pattern Recognition |
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DAG; 600.121 |
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Admin @ si @ GBK2020 |
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3495 |
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Author |
Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas |
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Title |
WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Springer |
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Analysis”, K. Alahari; C.V. Jawahar |
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Series on Advances in Computer Vision and Pattern Recognition |
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DAG; 600.121 |
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Admin @ si @ AGG2020 |
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3496 |
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Author |
Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe |
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Title |
Retrieval Guided Unsupervised Multi-domain Image to Image Translation |
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Conference Article |
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2020 |
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28th ACM International Conference on Multimedia |
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Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. |
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ACM |
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DAG; 600.121 |
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Call Number |
Admin @ si @ GLN2020 |
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3497 |
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Author |
Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer |
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Title |
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches. |
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Conference Article |
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Year |
2020 |
Publication |
International Workshop on Artificial Intelligence for Healthcare Applications |
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12661 |
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476-489 |
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Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field. |
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Virtual; January 2021 |
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ICPRW |
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DAG; 600.121; 600.140; |
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Admin @ si @ BCF2020 |
Serial |
3508 |
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Author |
Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados |
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Title |
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents |
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Conference Article |
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Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
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The use of administrative documents to communicate and leave record of business information requires of methods
able to automatically extract and understand the content from
such documents in a robust and efficient way. In addition,
the semi-structured nature of these reports is specially suited
for the use of graph-based representations which are flexible
enough to adapt to the deformations from the different document
templates. Moreover, Graph Neural Networks provide the proper
methodology to learn relations among the data elements in
these documents. In this work we study the use of Graph
Neural Network architectures to tackle the problem of entity
recognition and relation extraction in semi-structured documents.
Our approach achieves state of the art results in the three
tasks involved in the process. Additionally, the experimentation
with two datasets of different nature demonstrates the good
generalization ability of our approach. |
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Virtual; January 2021 |
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ICPR |
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DAG; 600.121 |
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no |
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Call Number |
Admin @ si @ CRV2020 |
Serial |
3509 |
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Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
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Title |
Learning Graph Edit Distance by Graph NeuralNetworks |
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Miscellaneous |
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Year |
2020 |
Publication |
Arxiv |
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The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. |
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DAG; 600.121; 600.140; 601.302 |
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Call Number |
Admin @ si @ RFL2020 |
Serial |
3555 |
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Author |
Klara Janousckova; Jiri Matas; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Text Recognition – Real World Data and Where to Find Them |
Type |
Conference Article |
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Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
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4489-4496 |
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We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets 1 1 Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya. |
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Virtual; January 2021 |
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DAG; 600.121; 600.129 |
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Call Number |
Admin @ si @ JMG2020 |
Serial |
3557 |
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Author |
Minesh Mathew; Ruben Tito; Dimosthenis Karatzas; R.Manmatha; C.V. Jawahar |
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Title |
Document Visual Question Answering Challenge 2020 |
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Conference Article |
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2020 |
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33rd IEEE Conference on Computer Vision and Pattern Recognition – Short paper |
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This paper presents results of Document Visual Question Answering Challenge organized as part of “Text and Documents in the Deep Learning Era” workshop, in CVPR 2020. The challenge introduces a new problem – Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image. On the other hand, the second task is set as a retrieval task where the question is posed over a collection of images. For the task 1 a new dataset is introduced comprising 50,000 questions-answer(s) pairs defined over 12,767 document images. For task 2 another dataset has been created comprising 20 questions over 14,362 document images which share the same document template. |
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CVPR |
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DAG; 600.121 |
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Admin @ si @ MTK2020 |
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3558 |
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Anjan Dutta; Zeynep Akata |
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Title |
Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval |
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2019 |
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32nd IEEE Conference on Computer Vision and Pattern Recognition |
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5089-5098 |
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Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets. |
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Long beach; California; USA; June 2019 |
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DAG; 600.141; 600.121 |
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Admin @ si @ DuA2019 |
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3268 |
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Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados |
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Title |
Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model |
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Journal Article |
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2019 |
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Pattern Recognition |
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PR |
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86 |
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27-36 |
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Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks |
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Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches. |
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DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 |
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Admin @ si @ TCF2019 |
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3166 |
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