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Author Gholamreza Anbarjafari; Sergio Escalera
Title Human-Robot Interaction: Theory and Application Type Book Whole
Year 2018 Publication Human-Robot Interaction: Theory and Application Abbreviated Journal
Volume Issue (down) Pages
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Abstract
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 978-1-78923-316-2 Medium
Area Expedition Conference
Notes HUPBA Approved no
Call Number Admin @ si @ AnE2018 Serial 3216
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Author Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi
Title WiCV 2018: The Fourth Women In Computer Vision Workshop Type Conference Article
Year 2018 Publication 4th Women in Computer Vision Workshop Abbreviated Journal
Volume Issue (down) Pages 1941-19412
Keywords Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning
Abstract We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
Address Salt Lake City; USA; June 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
Area Expedition Conference WiCV
Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ DBR2018 Serial 3222
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Author Arnau Baro; Pau Riba; Alicia Fornes
Title A Starting Point for Handwritten Music Recognition Type Conference Article
Year 2018 Publication 1st International Workshop on Reading Music Systems Abbreviated Journal
Volume Issue (down) Pages 5-6
Keywords Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA
Abstract In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community.
Address Paris; France; September 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
Area Expedition Conference WORMS
Notes DAG; 600.097; 601.302; 601.330; 600.121 Approved no
Call Number Admin @ si @ BRF2018 Serial 3223
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Author Laura Lopez-Fuentes; Alessandro Farasin; Harald Skinnemoen; Paolo Garza
Title Deep Learning models for passability detection of flooded roads Type Conference Article
Year 2018 Publication MediaEval 2018 Multimedia Benchmark Workshop Abbreviated Journal
Volume 2283 Issue (down) Pages
Keywords
Abstract In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task.
Address Sophia Antipolis; France; October 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
Area Expedition Conference MediaEval
Notes LAMP; 600.084; 600.109; 600.120 Approved no
Call Number Admin @ si @ LFS2018 Serial 3224
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Author Anjan Dutta; Hichem Sahbi
Title Stochastic Graphlet Embedding Type Journal Article
Year 2018 Publication IEEE Transactions on Neural Networks and Learning Systems Abbreviated Journal TNNLS
Volume Issue (down) Pages 1-14
Keywords Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality
Abstract Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider
these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When
combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.
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; 602.167; 602.168; 600.097; 600.121 Approved no
Call Number Admin @ si @ DuS2018 Serial 3225
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Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes
Title Optical Music Recognition by Long Short-Term Memory Networks Type Book Chapter
Year 2018 Publication Graphics Recognition. Current Trends and Evolutions Abbreviated Journal
Volume 11009 Issue (down) Pages 81-95
Keywords Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory
Abstract Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach.
Address
Corporate Author Thesis
Publisher Springer Place of Publication Editor A. Fornes, B. Lamiroy
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-030-02283-9 Medium
Area Expedition Conference GREC
Notes DAG; 600.097; 601.302; 601.330; 600.121 Approved no
Call Number Admin @ si @ BRC2018 Serial 3227
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Author Abel Gonzalez-Garcia; Davide Modolo; Vittorio Ferrari
Title Objects as context for detecting their semantic parts Type Conference Article
Year 2018 Publication 31st IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue (down) Pages 6907 - 6916
Keywords Proposals; Semantics; Wheels; Automobiles; Context modeling; Task analysis; Object detection
Abstract We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to
detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare
to other part detection methods on both PASCAL-Part and CUB200-2011 datasets.
Address Salt Lake City; USA; June 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
Area Expedition Conference CVPR
Notes LAMP; 600.109; 600.120 Approved no
Call Number Admin @ si @ GMF2018 Serial 3229
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Author Antonio Lopez
Title Pedestrian Detection Systems Type Book Chapter
Year 2018 Publication Wiley Encyclopedia of Electrical and Electronics Engineering Abbreviated Journal
Volume Issue (down) Pages
Keywords
Abstract Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning.
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 ADAS; 600.118 Approved no
Call Number Admin @ si @ Lop2018 Serial 3230
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Author Gemma 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 (down) 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.
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 ICPR
Notes ADAS; 600.086; 600.130; 600.118 Approved no
Call Number Admin @ si @ RLM2018 Serial 3232
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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 (down) 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.
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 MILAB; no proj Approved no
Call Number Admin @ si @ KJR2018 Serial 3235
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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 (down) 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
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 (down) Pages 161-174
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Abstract
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
Area Expedition Conference
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 (down) 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
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
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 (down) 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
Series Volume Series Issue Edition
ISSN ISBN Medium
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 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 (down) 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
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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