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Author Gema Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo
Title 2D-to-3D Facial Expression Transfer Type Conference Article
Year 2018 Publication 24th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 2008 - 2013
Keywords
Abstract Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an
input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular
mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic
equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops.
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Publisher Place of Publication Editor
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Area Expedition Conference ICPR
Notes ADAS; 600.086; 600.130; 600.118 Approved no
Call Number Admin @ si @ RLM2018 Serial 3232
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Author Lasse Martensson; Ekta Vats; Anders Hast; Alicia Fornes
Title In Search of the Scribe: Letter Spotting as a Tool for Identifying Scribes in Large Handwritten Text Corpora Type Journal
Year 2019 Publication Journal for Information Technology Studies as a Human Science Abbreviated Journal HUMAN IT
Volume 14 Issue 2 Pages 95-120
Keywords Scribal attribution/ writer identification; digital palaeography; word spotting; mediaeval charters; mediaeval manuscripts
Abstract In this article, a form of the so-called word spotting-method is used on a large set of handwritten documents in order to identify those that contain script of similar execution. The point of departure for the investigation is the mediaeval Swedish manuscript Cod. Holm. D 3. The main scribe of this manuscript has yet not been identified in other documents. The current attempt aims at localising other documents that display a large degree of similarity in the characteristics of the script, these being possible candidates for being executed by the same hand. For this purpose, the method of word spotting has been employed, focusing on individual letters, and therefore the process is referred to as letter spotting in the article. In this process, a set of ‘g’:s, ‘h’:s and ‘k’:s have been selected as templates, and then a search has been made for close matches among the mediaeval Swedish charters. The search resulted in a number of charters that displayed great similarities with the manuscript D 3. The used letter spotting method thus proofed to be a very efficient sorting tool localising similar script samples.
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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 (down)
Area Expedition Conference
Notes DAG; 600.097; 600.140; 600.121 Approved no
Call Number Admin @ si @ MVH2019 Serial 3234
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Author Md.Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig
Title CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ KJR2018 Serial 3235
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Author Eduardo Aguilar; Beatriz Remeseiro; Marc Bolaños; Petia Radeva
Title Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants Type Journal Article
Year 2018 Publication IEEE Transactions on Multimedia Abbreviated Journal
Volume 20 Issue 12 Pages 3266 - 3275
Keywords
Abstract The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments.
Address
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ ARB2018 Serial 3236
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Author Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas
Title Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets Type Conference Article
Year 2018 Publication International Workshop on Artificial Intelligence and Pattern Recognition Abbreviated Journal
Volume 11047 Issue Pages 34-42
Keywords Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis
Abstract The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall.
Address Cuba; September 2018
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 (down)
Area Expedition Conference IWAIPR
Notes MILAB; no menciona Approved no
Call Number Admin @ si @ BGÑ2018 Serial 3237
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Author Marçal Rusiñol; Lluis Gomez
Title Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos Type Journal
Year 2018 Publication Revista anual de la Asociación de Archiveros de Castilla y León Abbreviated Journal
Volume 21 Issue Pages 161-174
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Abstract
Address
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Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ RuG2018 Serial 3239
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Author Ozan Caglayan; Adrien Bardet; Fethi Bougares; Loic Barrault; Kai Wang; Marc Masana; Luis Herranz; Joost Van de Weijer
Title LIUM-CVC Submissions for WMT18 Multimodal Translation Task Type Conference Article
Year 2018 Publication 3rd Conference on Machine Translation Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This year we propose several modifications to our previou multimodal attention architecture in order to better integrate convolutional features and refine them using encoder-side information. Our final constrained submissions
ranked first for English→French and second for English→German language pairs among the constrained submissions according to the automatic evaluation metric METEOR.
Address Brussels; Belgium; October 2018
Corporate Author Thesis
Publisher Place of Publication Editor
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Area Expedition Conference WMT
Notes LAMP; 600.106; 600.120 Approved no
Call Number Admin @ si @ CBB2018 Serial 3240
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Author Aymen Azaza; Joost Van de Weijer; Ali Douik; Marc Masana
Title Context Proposals for Saliency Detection Type Journal Article
Year 2018 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 174 Issue Pages 1-11
Keywords
Abstract One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions
exist which potentially can all be salient. One way to prevent an exhaustive
search over all object regions is by using object proposal algorithms. These
return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated.
In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD).
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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ISSN ISBN Medium (down)
Area Expedition Conference
Notes LAMP; 600.109; 600.109; 600.120 Approved no
Call Number Admin @ si @ AWD2018 Serial 3241
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Author Hans Stadthagen-Gonzalez; M. Carmen Parafita; C. Alejandro Parraga; Markus F. Damian
Title Testing alternative theoretical accounts of code-switching: Insights from comparative judgments of adjective noun order Type Journal Article
Year 2019 Publication International journal of bilingualism: interdisciplinary studies of multilingual behaviour Abbreviated Journal IJB
Volume 23 Issue 1 Pages 200-220
Keywords
Abstract Objectives:
Spanish and English contrast in adjective–noun word order: for example, brown dress (English) vs. vestido marrón (‘dress brown’, Spanish). According to the Matrix Language model (MLF) word order in code-switched sentences must be compatible with the word order of the matrix language, but working within the minimalist program (MP), Cantone and MacSwan arrived at the descriptive generalization that the position of the noun phrase relative to the adjective is determined by the adjective’s language. Our aim is to evaluate the predictions derived from these two models regarding adjective–noun order in Spanish–English code-switched sentences.
Methodology:
We contrasted the predictions from both models regarding the acceptability of code-switched sentences with different adjective–noun orders that were compatible with the MP, the MLF, both, or none. Acceptability was assessed in Experiment 1 with a 5-point Likert and in Experiment 2 with a 2-Alternative Forced Choice (2AFC) task.
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Notes NEUROBIT; no menciona Approved no
Call Number Admin @ si @ SPP2019 Serial 3242
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Author Lu Yu; Yongmei Cheng; Joost Van de Weijer
Title Weakly Supervised Domain-Specific Color Naming Based on Attention Type Conference Article
Year 2018 Publication 24th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 3019 - 3024
Keywords
Abstract The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.
Address Beijing; August 2018
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 (down)
Area Expedition Conference ICPR
Notes LAMP; 600.109; 602.200; 600.120 Approved no
Call Number Admin @ si @ YCW2018 Serial 3243
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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.
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Notes MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ CSV2021 Serial 3577
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Author Ana Maria Ares; Jorge Bernal; Maria Jesus Nozal; F. Javier Sanchez; Jose Bernal
Title Results of the use of Kahoot! gamification tool in a course of Chemistry Type Conference Article
Year 2018 Publication 4th International Conference on Higher Education Advances Abbreviated Journal
Volume Issue Pages 1215-1222
Keywords
Abstract The present study examines the use of Kahoot! as a gamification tool to explore mixed learning strategies. We analyze its use in two different groups of a theoretical subject of the third course of the Degree in Chemistry. An empirical-analytical methodology was used using Kahoot! in two different groups of students, with different frequencies. The academic results of these two group of students were compared between them and with those obtained in the previous course, in which Kahoot! was not employed, with the aim of measuring the evolution in the students´ knowledge. The results showed, in all cases, that the use of Kahoot! has led to a significant increase in the overall marks, and in the number of students who passed the subject. Moreover, some differences were also observed in students´ academic performance according to the group. Finally, it can be concluded that the use of a gamification tool (Kahoot!) in a university classroom had generally improved students´ learning and marks, and that this improvement is more prevalent in those students who have achieved a better Kahoot! performance.
Address Valencia; June 2018
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium (down)
Area Expedition Conference HEAD
Notes MV; no proj Approved no
Call Number Admin @ si @ ABN2018 Serial 3246
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Author Xinhang Song; Shuqiang Jiang; Luis Herranz; Chengpeng Chen
Title Learning Effective RGB-D Representations for Scene Recognition Type Journal Article
Year 2019 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 28 Issue 2 Pages 980-993
Keywords
Abstract Deep convolutional networks can achieve impressive results on RGB scene recognition thanks to large data sets such as places. In contrast, RGB-D scene recognition is still underdeveloped in comparison, due to two limitations of RGB-D data we address in this paper. The first limitation is the lack of depth data for training deep learning models. Rather than fine tuning or transferring RGB-specific features, we address this limitation by proposing an architecture and a two-step training approach that directly learns effective depth-specific features using weak supervision via patches. The resulting RGB-D model also benefits from more complementary multimodal features. Another limitation is the short range of depth sensors (typically 0.5 m to 5.5 m), resulting in depth images not capturing distant objects in the scenes that RGB images can. We show that this limitation can be addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scenes. Focusing on this scenario, we introduce the ISIA RGB-D video data set to evaluate RGB-D scene recognition with videos. Our video recognition architecture combines convolutional and recurrent neural networks that are trained in three steps with increasingly complex data to learn effective features (i.e., patches, frames, and sequences). Our approach obtains the state-of-the-art performances on RGB-D image (NYUD2 and SUN RGB-D) and video (ISIA RGB-D) scene recognition.
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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Notes LAMP; 600.141; 600.120 Approved no
Call Number Admin @ si @ SJH2019 Serial 3247
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Author Hugo Prol; Vincent Dumoulin; Luis Herranz
Title Cross-Modulation Networks for Few-Shot Learning Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art.
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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ISSN ISBN Medium (down)
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Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ PDH2018 Serial 3248
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Author Chenshen Wu; Luis Herranz; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu
Title Memory Replay GANs: Learning to Generate New Categories without Forgetting Type Conference Article
Year 2018 Publication 32nd Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages 5966-5976
Keywords
Abstract Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (ie forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories.
Address Montreal; Canada; December 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium (down)
Area Expedition Conference NIPS
Notes LAMP; 600.106; 600.109; 602.200; 600.120 Approved no
Call Number Admin @ si @ WHL2018 Serial 3249
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