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Author Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados edit   pdf
openurl 
  Title Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning Type Conference Article
  Year 2019 Publication 13th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages 80-85  
  Keywords Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning  
  Abstract With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results.
 
  Address Sydney; Australia; September 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference GREC  
  Notes (up) DAG; 600.140; 601.302; 600.121 Approved no  
  Call Number Admin @ si @ BRF2019 Serial 3354  
Permanent link to this record
 

 
Author Lei Kang; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol edit   pdf
url  openurl
  Title Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture Type Journal Article
  Year 2021 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 112 Issue Pages 107790  
  Keywords  
  Abstract Sequence-to-sequence models have recently become very popular for tackling
handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging
problem. The main challenge faced when training a language model is to
deal with the language model corpus which is usually different to the one
used for training the handwritten word recognition system. Thus, the bias
between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this
work, we introduce Candidate Fusion, a novel way to integrate an external
language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to
the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two
improvements. On the one hand, the sequence-to-sequence recognizer has
the flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information provided
by the language model. On the other hand, the external language model
has the ability to adapt itself to the training corpus and even learn the
most commonly errors produced from the recognizer. Finally, by conducting
comprehensive experiments, the Candidate Fusion proves to outperform the
state-of-the-art language models for handwritten word recognition tasks.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (up) DAG; 600.140; 601.302; 601.312; 600.121 Approved no  
  Call Number Admin @ si @ KRV2021 Serial 3343  
Permanent link to this record
 

 
Author Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados edit   pdf
url  doi
openurl 
  Title Table Detection in Invoice Documents by Graph Neural Networks Type Conference Article
  Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 122-127  
  Keywords  
  Abstract Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text
reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity
of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research.
 
  Address Sydney; Australia; September 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes (up) DAG; 600.140; 601.302; 602.167; 600.121; 600.141 Approved no  
  Call Number Admin @ si @ RDG2019 Serial 3355  
Permanent link to this record
 

 
Author Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados edit   pdf
openurl 
  Title A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages Type Journal Article
  Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 136 Issue Pages 219-227  
  Keywords  
  Abstract In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (up) DAG; 600.140; 601.311; 600.121 Approved no  
  Call Number Admin @ si @ CFV2020 Serial 3451  
Permanent link to this record
 

 
Author Manuel Carbonell; Joan Mas; Mauricio Villegas; Alicia Fornes; Josep Llados edit   pdf
url  doi
openurl 
  Title End-to-End Handwritten Text Detection and Transcription in Full Pages Type Conference Article
  Year 2019 Publication 2nd International Workshop on Machine Learning Abbreviated Journal  
  Volume 5 Issue Pages 29-34  
  Keywords Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning  
  Abstract When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately.
 
  Address Sydney; Australia; September 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR WML  
  Notes (up) DAG; 600.140; 601.311; 600.140 Approved no  
  Call Number Admin @ si @ CMV2019 Serial 3353  
Permanent link to this record
 

 
Author Jialuo Chen; M.A.Souibgui; Alicia Fornes; Beata Megyesi edit   pdf
openurl 
  Title A Web-based Interactive Transcription Tool for Encrypted Manuscripts Type Conference Article
  Year 2020 Publication 3rd International Conference on Historical Cryptology Abbreviated Journal  
  Volume Issue Pages 52-59  
  Keywords  
  Abstract Manual transcription of handwritten text is a time consuming task. In the case of encrypted manuscripts, the recognition is even more complex due to the huge variety of alphabets and symbol sets. To speed up and ease this process, we present a web-based tool aimed to (semi)-automatically transcribe the encrypted sources. The user uploads one or several images of the desired encrypted document(s) as input, and the system returns the transcription(s). This process is carried out in an interactive fashion with
the user to obtain more accurate results. For discovering and testing, the developed web tool is freely available.
 
  Address Virtual; June 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference HistoCrypt  
  Notes (up) DAG; 600.140; 602.230; 600.121 Approved no  
  Call Number Admin @ si @ CSF2020 Serial 3447  
Permanent link to this record
 

 
Author Anjan Dutta; Zeynep Akata edit   pdf
url  doi
openurl 
  Title Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval Type Conference Article
  Year 2019 Publication 32nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 5089-5098  
  Keywords  
  Abstract 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.  
  Address Long beach; California; USA; June 2019  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CVPR  
  Notes (up) DAG; 600.141; 600.121 Approved no  
  Call Number Admin @ si @ DuA2019 Serial 3268  
Permanent link to this record
 

 
Author Minesh Mathew; Viraj Bagal; Ruben Tito; Dimosthenis Karatzas; Ernest Valveny; C.V. Jawahar edit   pdf
url  doi
openurl 
  Title InfographicVQA Type Conference Article
  Year 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1697-1706  
  Keywords Document Analysis Datasets; Evaluation and Comparison of Vision Algorithms; Vision and Languages  
  Abstract Infographics communicate information using a combination of textual, graphical and visual elements. This work explores the automatic understanding of infographic images by using a Visual Question Answering technique. To this end, we present InfographicVQA, a new dataset comprising a diverse collection of infographics and question-answer annotations. The questions require methods that jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with an emphasis on questions that require elementary reasoning and basic arithmetic skills. For VQA on the dataset, we evaluate two Transformer-based strong baselines. Both the baselines yield unsatisfactory results compared to near perfect human performance on the dataset. The results suggest that VQA on infographics--images that are designed to communicate information quickly and clearly to human brain--is ideal for benchmarking machine understanding of complex document images. The dataset is available for download at docvqa. org  
  Address Virtual; Waikoloa; Hawai; USA; January 2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes (up) DAG; 600.155 Approved no  
  Call Number MBT2022 Serial 3625  
Permanent link to this record
 

 
Author Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  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  
  Volume Issue 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  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes (up) DAG; 600.155; 302.105 Approved no  
  Call Number Admin @ si @ BGK2022 Serial 3662  
Permanent link to this record
 

 
Author Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching Type Conference Article
  Year 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1391-1400  
  Keywords Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning  
  Abstract The task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a large improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. The code for our new metric can be found at github. com/furkanbiten/ncsmetric and the model implementation at github. com/andrespmd/semanticadaptive_margin.  
  Address Virtual; Waikoloa; Hawai; USA; January 2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes (up) DAG; 600.155; 302.105; Approved no  
  Call Number Admin @ si @ BMG2022 Serial 3663  
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