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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 | ||
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| 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 | ||||
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| Area | Expedition | Conference  |
HEAD | ||
| Notes | MV; no proj | Approved | no | ||
| Call Number | Admin @ si @ ABN2018 | Serial | 3246 | ||
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| Author | Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora | ||||
| Title | Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts | Type | Conference Article | ||
| Year | 2018 | Publication | 16th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 528-533 | ||
| Keywords | Crowdsourcing; Gamification; Handwritten documents; Performance evaluation | ||||
| Abstract | Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance. |
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| Address | Niagara Falls, USA; August 2018 | ||||
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| Area | Expedition | Conference |
ICFHR | ||
| Notes | DAG; 600.097; 603.057; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CRF2018 | Serial | 3169 | ||
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| Author | Stefan Schurischuster; Beatriz Remeseiro; Petia Radeva; Martin Kampel | ||||
| Title | A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees | Type | Conference Article | ||
| Year | 2018 | Publication | 15th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 10882 | Issue | Pages | 465-473 | |
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| Abstract | Varroa destructor is a parasite harming bee colonies. As the worldwide bee population is in danger, beekeepers as well as researchers are looking for methods to monitor the health of bee hives. In this context, we present a preliminary study to detect parasites on bee videos by means of image analysis and machine learning techniques. For this purpose, each video frame is analyzed individually to extract bee image patches, which are then processed to compute image descriptors and finally classified into mite and no mite bees. The experimental results demonstrated the adequacy of the proposed method, which will be a perfect stepping stone for a further bee monitoring system. | ||||
| Address | Povoa de Varzim; Portugal; June 2018 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference |
ICIAR | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SRR2018a | Serial | 3110 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Vegetation Index Estimation from Monospectral Images | Type | Conference Article | ||
| Year | 2018 | Publication | 15th International Conference on Images Analysis and Recognition | Abbreviated Journal | |
| Volume | 10882 | Issue | Pages | 353-362 | |
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| Abstract | This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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| Address | Povoa de Varzim; Portugal; June 2018 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference |
ICIAR | ||
| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSV2018c | Serial | 3196 | ||
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| Author | Marco Buzzelli; Joost Van de Weijer; Raimondo Schettini | ||||
| Title | Learning Illuminant Estimation from Object Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 25th International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 3234 - 3238 | ||
| Keywords | Illuminant estimation; computational color constancy; semi-supervised learning; deep learning; convolutional neural networks | ||||
| Abstract | In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep
learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions. |
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| Address | Athens; Greece; October 2018 | ||||
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| Area | Expedition | Conference |
ICIP | ||
| Notes | LAMP; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ BWS2018 | Serial | 3157 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Near InfraRed Imagery Colorization | Type | Conference Article | ||
| Year | 2018 | Publication | 25th International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 2237 - 2241 | ||
| Keywords | Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization | ||||
| Abstract | This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics. |
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| Address | Athens; Greece; October 2018 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference |
ICIP | ||
| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSV2018b | Serial | 3195 | ||
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| Author | Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal | ||||
| Title | Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch | Type | Conference Article | ||
| Year | 2018 | Publication | 24th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 916 - 921 | ||
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| Abstract | In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets. | ||||
| Address | Beijing; China; August 2018 | ||||
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| Area | Expedition | Conference |
ICPR | ||
| Notes | DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ DDG2018b | Serial | 3152 | ||
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| Author | Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov | ||||
| Title | Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting | Type | Conference Article | ||
| Year | 2018 | Publication | 24th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2262-2268 | ||
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| Abstract | In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of
a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting. |
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| Area | Expedition | Conference |
ICPR | ||
| Notes | LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ LMH2018 | Serial | 3160 | ||
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| Author | Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes | ||||
| Title | Learning Graph Distances with Message Passing Neural Networks | Type | Conference Article | ||
| Year | 2018 | Publication | 24th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2239-2244 | ||
| Keywords | ★Best Paper Award★ | ||||
| Abstract | Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high
computational complexity, which makes it difficult to apply these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with (approximate) graph edit distance benchmarks. |
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| Address | Beijing; China; August 2018 | ||||
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| Area | Expedition | Conference |
ICPR | ||
| Notes | DAG; 600.097; 603.057; 601.302; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RFL2018 | Serial | 3168 | ||
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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 | Pages | 2008 - 2013 | ||
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| 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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| Area | Expedition | Conference |
ICPR | ||
| Notes | MSIAU; 600.086; 600.130; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ RLM2018 | Serial | 3232 | ||
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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 | ||
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| 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 | ||||
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ICPR | ||
| Notes | LAMP; 600.109; 602.200; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ YCW2018 | Serial | 3243 | ||
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| Author | Gabriela Ramirez; Esau Villatoro; Bogdan Ionescu; Hugo Jair Escalante; Sergio Escalera; Martha Larson; Henning Muller; Isabelle Guyon | ||||
| Title | Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contes | Type | Conference Article | ||
| Year | 2018 | Publication | Multimedia Information Processing for Personality and Social Networks Analysis (MIPPSNA 2018) | Abbreviated Journal | |
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| Address | Beijing; China; August 2018 | ||||
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ICPRW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ RVI2018 | Serial | 3211 | ||
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| Author | Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy | ||||
| Title | End-to-end Driving via Conditional Imitation Learning | Type | Conference Article | ||
| Year | 2018 | Publication | IEEE International Conference on Robotics and Automation | Abbreviated Journal | |
| Volume | Issue | Pages | 4693 - 4700 | ||
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| Abstract | Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL | ||||
| Address | Brisbane; Australia; May 2018 | ||||
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| Area | Expedition | Conference |
ICRA | ||
| Notes | ADAS; 600.116; 600.124; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ CML2018 | Serial | 3108 | ||
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| Author | Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce | ||||
| Title | Libraries as New Innovation Hubs: The Library Living Lab | Type | Conference Article | ||
| Year | 2018 | Publication | 30th ISPIM Innovation Conference | Abbreviated Journal | |
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| Abstract | Libraries are in deep transformation both in EU and around the world, and they are thriving within a great window of opportunity for innovation. In this paper, we show how the Library Living Lab in Barcelona participated of this changing scenario and contributed to create the Bibliolab program, where more than 200 public libraries give voice to their users in a global user-centric innovation initiative, using technology as enabling factor. The Library Living Lab is a real 4-helix implementation where Universities, Research Centers, Public Administration, Companies and the Neighbors are joint together to explore how technology transforms the cultural experience of people. This case is an example of scalability and provides reference tools for policy making, sustainability, user engage methodologies and governance. We provide specific examples of new prototypes and services that help to understand how to redefine the role of the Library as a real hub for social innovation. | ||||
| Address | Stockholm; May 2018 | ||||
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| Area | Expedition | Conference |
ISPIM | ||
| Notes | DAG; MV; 600.097; 600.121; 600.129;SIAI | Approved | no | ||
| Call Number | Admin @ si @ VKV2018b | Serial | 3154 | ||
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| Author | Zhijie Fang; Antonio Lopez | ||||
| Title | Is the Pedestrian going to Cross? Answering by 2D Pose Estimation | Type | Conference Article | ||
| Year | 2018 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
| Volume | Issue | Pages | 1271 - 1276 | ||
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| Abstract | Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. | ||||
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IV | ||
| Notes | ADAS; 600.124; 600.116; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ FaL2018 | Serial | 3181 | ||
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