| Records |
| Author |
Manuel Carbonell |
| Title |
Neural Information Extraction from Semi-structured Documents A |
Type |
Book Whole |
| Year |
2020 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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| Abstract |
Sectors as fintech, legaltech or insurance process an inflow of millions of forms, invoices, id documents, claims or similar every day. Together with these, historical archives provide gigantic amounts of digitized documents containing useful information that needs to be stored in machine encoded text with a meaningful structure. This procedure, known as information extraction (IE) comprises the steps of localizing and recognizing text, identifying named entities contained in it and optionally finding relationships among its elements. In this work we explore multi-task neural models at image and graph level to solve all steps in a unified way. While doing so we find benefits and limitations of these end-to-end approaches in comparison with sequential separate methods. More specifically, we first propose a method to produce textual as well as semantic labels with a unified model from handwritten text line images. We do so with the use of a convolutional recurrent neural model trained with connectionist temporal classification to predict the textual as well as semantic information encoded in the images. Secondly, motivated by the success of this approach we investigate the unification of the localization and recognition tasks of handwritten text in full pages with an end-to-end model, observing benefits in doing so. Having two models that tackle information extraction subsequent task pairs in an end-to-end to end manner, we lastly contribute with a method to put them all together in a single neural network to solve the whole information extraction pipeline in a unified way. Doing so we observe some benefits and some limitations in the approach, suggesting that in certain cases it is beneficial to train specialized models that excel at a single challenging task of the information extraction process, as it can be the recognition of named entities or the extraction of relationships between them. For this reason we lastly study the use of the recently arrived graph neural network architectures for the semantic tasks of the information extraction process, which are recognition of named entities and relation extraction, achieving promising results on the relation extraction part. |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
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Editor |
Alicia Fornes;Mauricio Villegas;Josep Llados |
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ISBN |
978-84-122714-1-6 |
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Conference |
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| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ Car20 |
Serial |
3483 |
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| Author |
Riccardo Del Chiaro; Bartlomiej Twardowski; Andrew Bagdanov; Joost Van de Weijer |
| Title |
Recurrent attention to transient tasks for continual image captioning |
Type |
Conference Article |
| Year |
2020 |
Publication |
34th Conference on Neural Information Processing Systems |
Abbreviated Journal |
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| Abstract |
Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper we take a systematic look at continual learning of LSTM-based models for image captioning. We propose an attention-based approach that explicitly accommodates the transient nature of vocabularies in continual image captioning tasks -- i.e. that task vocabularies are not disjoint. We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight egularization and knowledge distillation to recurrent continual learning problems. We apply our approaches to incremental image captioning problem on two new continual learning benchmarks we define using the MS-COCO and Flickr30 datasets. Our results demonstrate that RATT is able to sequentially learn five captioning tasks while incurring no forgetting of previously learned ones. |
| Address |
virtual; December 2020 |
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Conference |
NEURIPS |
| Notes |
LAMP; 600.120 |
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no |
| Call Number |
Admin @ si @ CTB2020 |
Serial |
3484 |
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| Author |
Yaxing Wang; Lu Yu; Joost Van de Weijer |
| Title |
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs |
Type |
Conference Article |
| Year |
2020 |
Publication |
34th Conference on Neural Information Processing Systems |
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Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Therefore, in this work, we propose a novel deep hierarchical Image-to-Image Translation method, called DeepI2I. We learn a model by leveraging hierarchical features: (a) structural information contained in the shallow layers and (b) semantic information extracted from the deep layers. To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs. Specifically, we leverage the discriminator of a pre-trained GANs (i.e. BigGAN or StyleGAN) to initialize both the encoder and the discriminator and the pre-trained generator to initialize the generator of our model. Applying knowledge transfer leads to an alignment problem between the encoder and generator. We introduce an adaptor network to address this. On many-class image-to-image translation on three datasets (Animal faces, Birds, and Foods) we decrease mFID by at least 35% when compared to the state-of-the-art. Furthermore, we qualitatively and quantitatively demonstrate that transfer learning significantly improves the performance of I2I systems, especially for small datasets. Finally, we are the first to perform I2I translations for domains with over 100 classes. |
| Address |
virtual; December 2020 |
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Conference |
NEURIPS |
| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ WYW2020 |
Serial |
3485 |
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| Author |
Yaxing Wang; Salman Khan; Abel Gonzalez-Garcia; Joost Van de Weijer; Fahad Shahbaz Khan |
| Title |
Semi-supervised Learning for Few-shot Image-to-Image Translation |
Type |
Conference Article |
| Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
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In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image translation, reducing the labeled data requirements for the target domain during inference. In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training. To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. We also apply a cycle consistency constraint to further exploit the information from unlabeled images, either from the same dataset or external. Additionally, we propose several structural modifications to facilitate the image translation task under these circumstances. Our semi-supervised method for few-shot image translation, called SEMIT, achieves excellent results on four different datasets using as little as 10% of the source labels, and matches the performance of the main fully-supervised competitor using only 20% labeled data. Our code and models are made public at: this https URL. |
| Address |
Virtual; June 2020 |
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Conference |
CVPR |
| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ WKG2020 |
Serial |
3486 |
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| Author |
Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez |
| Title |
Action-Based Representation Learning for Autonomous Driving |
Type |
Conference Article |
| Year |
2020 |
Publication |
Conference on Robot Learning |
Abbreviated Journal |
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| Abstract |
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet). |
| Address |
virtual; November 2020 |
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Conference |
CORL |
| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ XCP2020 |
Serial |
3487 |
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| Author |
Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
| Title |
Monitoring War Destruction from Space: A Machine Learning Approach |
Type |
Miscellaneous |
| Year |
2020 |
Publication |
Arxiv |
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Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively. |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ MGH2020 |
Serial |
3489 |
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| Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
| Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
4022-4032 |
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| Address |
Virtual; January 2021 |
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Conference |
WACV |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ MDB2021 |
Serial |
3491 |
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| Author |
Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas |
| Title |
StacMR: Scene-Text Aware Cross-Modal Retrieval |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
2219-2229 |
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| Address |
Virtual; January 2021 |
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Conference |
WACV |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ MRG2021a |
Serial |
3492 |
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| Author |
Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas |
| Title |
12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding |
Type |
Book Chapter |
| Year |
2020 |
Publication |
Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Springer |
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Editor |
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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no |
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GNR2020 |
Serial |
3494 |
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| Author |
Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas |
| Title |
Historical review of scene text detection research |
Type |
Book Chapter |
| Year |
2020 |
Publication |
Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Springer |
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Editor |
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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no |
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Admin @ si @ GBK2020 |
Serial |
3495 |
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| Author |
Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas |
| Title |
WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval |
Type |
Book Chapter |
| Year |
2020 |
Publication |
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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| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ AGG2020 |
Serial |
3496 |
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| Author |
Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe |
| Title |
Retrieval Guided Unsupervised Multi-domain Image to Image Translation |
Type |
Conference Article |
| Year |
2020 |
Publication |
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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Conference |
ACM |
| Notes |
DAG; 600.121 |
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no |
| Call Number |
Admin @ si @ GLN2020 |
Serial |
3497 |
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| Author |
Minesh Mathew; Dimosthenis Karatzas; C.V. Jawahar |
| Title |
DocVQA: A Dataset for VQA on Document Images |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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2200-2209 |
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We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa. org |
| Address |
Virtual; January 2021 |
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WACV |
| Notes |
DAG; 600.121 |
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no |
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Admin @ si @ MKJ2021 |
Serial |
3498 |
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| Author |
Tomas Sixta; Julio C. S. Jacques Junior; Pau Buch Cardona; Eduard Vazquez; Sergio Escalera |
| Title |
FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition |
Type |
Conference Article |
| Year |
2020 |
Publication |
ECCV Workshops |
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12540 |
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463-481 |
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This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too. |
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Virtual; August 2020 |
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LNCS |
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ECCVW |
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HUPBA |
Approved |
no |
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Admin @ si @ SJB2020 |
Serial |
3499 |
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| Author |
Zhengying Liu; Zhen Xu; Shangeth Rajaa; Meysam Madadi; Julio C. S. Jacques Junior; Sergio Escalera; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu; Isabelle Guyon |
| Title |
Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019 |
Type |
Conference Article |
| Year |
2020 |
Publication |
Proceedings of Machine Learning Research |
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123 |
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242-252 |
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We present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The first 4 challenges concern each a specific application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still on-going and we only present its design. Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior success (image, video, text, speech, etc); (2) a novel any-time learning framework, which opens doors for further theoretical consideration; (3) a repository of around 100 datasets (from all above domains) over half of which are released as public datasets to enable research on meta-learning; (4) analyses revealing that winning solutions generalize to new unseen datasets, validating progress towards universal AutoML solution; (5) open-sourcing of the challenge platform, the starting kit, the dataset formatting toolkit, and all winning solutions (All information available at {autodl.chalearn.org}). |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
NEURIPS |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ LXR2020 |
Serial |
3500 |
| Permanent link to this record |
