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Author Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
Title Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting Type Conference Article
Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12822 Issue Pages 381–395
Keywords
Abstract In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
Address Lausanne; Suissa; September 2021
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ RMG2021 Serial (up) 3572
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Author Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
Title DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis Type Conference Article
Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12823 Issue Pages 555–568
Keywords
Abstract Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind.
Address Lausanne; Suissa; September 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ BRL2021a Serial (up) 3573
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Author Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
Title Beyond Document Object Detection: Instance-Level Segmentation of Complex Layouts Type Journal Article
Year 2021 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR
Volume 24 Issue Pages 269–281
Keywords
Abstract Information extraction is a fundamental task of many business intelligence services that entail massive document processing. Understanding a document page structure in terms of its layout provides contextual support which is helpful in the semantic interpretation of the document terms. In this paper, inspired by the progress of deep learning methodologies applied to the task of object recognition, we transfer these models to the specific case of document object detection, reformulating the traditional problem of document layout analysis. Moreover, we importantly contribute to prior arts by defining the task of instance segmentation on the document image domain. An instance segmentation paradigm is especially important in complex layouts whose contents should interact for the proper rendering of the page, i.e., the proper text wrapping around an image. Finally, we provide an extensive evaluation, both qualitative and quantitative, that demonstrates the superior performance of the proposed methodology over the current state of the art.
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 DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ BRL2021b Serial (up) 3574
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Author Kai Wang; Joost Van de Weijer; Luis Herranz
Title ACAE-REMIND for online continual learning with compressed feature replay Type Journal Article
Year 2021 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 150 Issue Pages 122-129
Keywords online continual learning; autoencoders; vector quantization
Abstract Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset.
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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 LAMP; 600.147; 601.379; 600.120; 600.141 Approved no
Call Number Admin @ si @ WWH2021 Serial (up) 3575
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Author Henry Velesaca; Patricia Suarez; Raul Mira; Angel Sappa
Title Computer Vision based Food Grain Classification: a Comprehensive Survey Type Journal Article
Year 2021 Publication Computers and Electronics in Agriculture Abbreviated Journal CEA
Volume 187 Issue Pages 106287
Keywords
Abstract This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches proposed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented.
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 MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ VSM2021 Serial (up) 3576
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Author Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca
Title Camera pose estimation in multi-view environments: From virtual scenarios to the real world Type Journal Article
Year 2021 Publication Image and Vision Computing Abbreviated Journal IVC
Volume 110 Issue Pages 104182
Keywords
Abstract This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ CSV2021 Serial (up) 3577
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Deep learning-based vegetation index estimation Type Book Chapter
Year 2021 Publication Generative Adversarial Networks for Image-to-Image Translation Abbreviated Journal
Volume Issue Pages 205-234
Keywords
Abstract Chapter 9
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor A.Solanki; A.Nayyar; M.Naved
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MSIAU; 600.122 Approved no
Call Number Admin @ si @ SSV2021a Serial (up) 3578
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud
Title Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation Type Conference Article
Year 2021 Publication 28th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 19-22
Keywords
Abstract This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.
Address Anchorage-Alaska; USA; September 2021
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 ICIP
Notes MSIAU; 600.130; 600.122; 601.349 Approved no
Call Number Admin @ si @ SSV2021b Serial (up) 3579
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Author Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Sabari Nathan; Priya Kansal; Armin Mehri; Parichehr Behjati Ardakani; A.Dalal; A.Akula; D.Sharma; S.Pandey; B.Kumar; J.Yao; R.Wu; KFeng; N.Li; Y.Zhao; H.Patel; V. Chudasama; K.Pjajapati; A.Sarvaiya; K.Upla; K.Raja; R.Ramachandra; C.Bush; F.Almasri; T.Vandamme; O.Debeir; N.Gutierrez; Q.Nguyen; W.Beksi
Title Thermal Image Super-Resolution Challenge – PBVS 2021 Type Conference Article
Year 2021 Publication Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal
Volume Issue Pages 4359-4367
Keywords
Abstract This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used; only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image; this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results outperformed those from the first challenge, thus showing an improvement in both evaluation metrics.
Address Virtual; June 2021
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 CVPRW
Notes MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ RSV2021 Serial (up) 3581
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Author Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa
Title MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution Type Conference Article
Year 2021 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages 2703-2712
Keywords
Abstract Lightweight super resolution networks have extremely importance for real-world applications. In recent years several SR deep learning approaches with outstanding achievement have been introduced by sacrificing memory and computational cost. To overcome this problem, a novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR and performs roughly similar to computationally expensive networks. Multi-Path Residual Network designs with a set of Residual concatenation Blocks stacked with Adaptive Residual Blocks: ($i$) to adaptively extract informative features and learn more expressive spatial context information; ($ii$) to better leverage multi-level representations before up-sampling stage; and ($iii$) to allow an efficient information and gradient flow within the network. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model. Extensive experiments show the superiority of our model against other SOTA SR approaches.
Address Virtual; January 2021
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 MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ MAS2021b Serial (up) 3582
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Author Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa
Title LiNet: A Lightweight Network for Image Super Resolution Type Conference Article
Year 2021 Publication 25th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 7196-7202
Keywords
Abstract This paper proposes a new lightweight network, LiNet, that enhancing technical efficiency in lightweight super resolution and operating approximately like very large and costly networks in terms of number of network parameters and operations. The proposed architecture allows the network to learn more abstract properties by avoiding low-level information via multiple links. LiNet introduces a Compact Dense Module, which contains set of inner and outer blocks, to efficiently extract meaningful information, to better leverage multi-level representations before upsampling stage, and to allow an efficient information and gradient flow within the network. Experiments on benchmark datasets show that the proposed LiNet achieves favorable performance against lightweight state-of-the-art methods.
Address Virtual; January 2021
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 MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ MAS2021a Serial (up) 3583
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Author Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat
Title Monitoring war destruction from space using machine learning Type Journal Article
Year 2021 Publication Proceedings of the National Academy of Sciences of the United States of America Abbreviated Journal PNAS
Volume 118 Issue 23 Pages e2025400118
Keywords
Abstract 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 label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.
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Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ MGH2021 Serial (up) 3584
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Author Bartlomiej Twardowski; Pawel Zawistowski; Szymon Zaborowski
Title Metric Learning for Session-Based Recommendations Type Conference Article
Year 2021 Publication 43rd edition of the annual BCS-IRSG European Conference on Information Retrieval Abbreviated Journal
Volume 12656 Issue Pages 650-665
Keywords Session-based recommendations; Deep metric learning; Learning to rank
Abstract Session-based recommenders, used for making predictions out of users’ uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study.
Address Virtual; March 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECIR
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ TZZ2021 Serial (up) 3586
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Author Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Fabio Ferreira; Isabelle Guyon; Sirui Hong; Frank Hutter; Rongrong Ji; Julio C. S. Jacques Junior; Ge Li; Marius Lindauer; Zhipeng Luo; Meysam Madadi; Thomas Nierhoff; Kangning Niu; Chunguang Pan; Danny Stoll; Sebastien Treguer; Jin Wang; Peng Wang; Chenglin Wu; Youcheng Xiong; Arber Zela; Yang Zhang
Title Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019 Type Journal Article
Year 2021 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 43 Issue 9 Pages 3108 - 3125
Keywords
Abstract This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a “meta-learner”, “data ingestor”, “model selector”, “model/learner”, and “evaluator”. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free “AutoDL self-service.”
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Publisher Place of Publication Editor
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LPX2021 Serial (up) 3587
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Author Albin Soutif; Marc Masana; Joost Van de Weijer; Bartlomiej Twardowski
Title On the importance of cross-task features for class-incremental learning Type Conference Article
Year 2021 Publication Theory and Foundation of continual learning workshop of ICML Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small.
Address Virtual; July 2021
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 ICMLW
Notes LAMP Approved no
Call Number Admin @ si @ SMW2021 Serial (up) 3588
Permanent link to this record