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Author |
C. Alejandro Parraga; Arash Akbarinia |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Colour Constancy as a Product of Dynamic Centre-Surround Adaptation |
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Conference Article |
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2016 |
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16th Annual meeting in Vision Sciences Society |
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16 |
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12 |
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Colour constancy refers to the human visual system's ability to preserve the perceived colour of objects despite changes in the illumination. Its exact mechanisms are unknown, although a number of systems ranging from retinal to cortical and memory are thought to play important roles. The strength of the perceptual shift necessary to preserve these colours is usually estimated by the vectorial distances from an ideal match (or canonical illuminant). In this work we explore how much of the colour constancy phenomenon could be explained by well-known physiological properties of V1 and V2 neurons whose receptive fields (RF) vary according to the contrast and orientation of surround stimuli. Indeed, it has been shown that both RF size and the normalization occurring between centre and surround in cortical neurons depend on the local properties of surrounding stimuli. Our stating point is the construction of a computational model which includes this dynamical centre-surround adaptation by means of two overlapping asymmetric Gaussian kernels whose variances are adjusted to the contrast of surrounding pixels to represent the changes in RF size of cortical neurons and the weights of their respective contributions are altered according to differences in centre-surround contrast and orientation. The final output of the model is obtained after convolving an image with this dynamical operator and an estimation of the illuminant is obtained by considering the contrast of the far surround. We tested our algorithm on naturalistic stimuli from several benchmark datasets. Our results show that although our model does not require any training, its performance against the state-of-the-art is highly competitive, even outperforming learning-based algorithms in some cases. Indeed, these results are very encouraging if we consider that they were obtained with the same parameters for all datasets (i.e. just like the human visual system operates). |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Florida; USA; May 2016 |
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NEUROBIT |
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Admin @ si @ PaA2016b |
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2901 |
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Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Stable Airway Center Tracking for Bronchoscopic Navigation |
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Conference Article |
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2016 |
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28th Conference of the international Society for Medical Innovation and Technology |
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Bronchoscopists use X‐ray fluoroscopy to guide bronchoscopes to the lesion to be biopsied without any kind of incisions. Reducing exposure to X‐ray is important for both patients and doctors but alternatives like electromagnetic navigation require specific equipment and increase the cost of the clinical procedure. We propose a guiding system based on the extraction of airway centers from intra‐operative videos. Such anatomical landmarks could be
matched to the airway centerline extracted from a pre‐planned CT to indicate the best path to the lesion. We present an extraction of lumen centers
from intra‐operative videos based on tracking of maximal stable regions of energy maps. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Delft; Rotterdam; Leiden; The Netherlands; October 2016 |
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IAM; |
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Admin @ si @ LSB2016a |
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2856 |
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Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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With whom do I interact with? Social interaction detection in egocentric photo-streams |
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2016 |
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23rd International Conference on Pattern Recognition |
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Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Cancun; Mexico; December 2016 |
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MILAB |
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Admin @ si @ADR2016a |
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2791 |
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Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Improving Text Proposals for Scene Images with Fully Convolutional Networks |
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2016 |
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23rd International Conference on Pattern Recognition Workshops |
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Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text
recognition. In this paper we propose an improvement over the original Text Proposals algorithm of [1], combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Cancun; Mexico; December 2016 |
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DAG; LAMP; 600.084 |
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Admin @ si @ BGN2016 |
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2823 |
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Hugo Jair Escalante; Victor Ponce; Jun Wan; Michael A. Riegler; Baiyu Chen; Albert Clapes; Sergio Escalera; Isabelle Guyon; Xavier Baro; Pal Halvorsen; Henning Muller; Martha Larson |
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Title |
ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An Overview |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition |
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This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from
short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost
200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to
allow researchers to keep making progress in the four tracks. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Cancun; Mexico; December 2016 |
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HuPBA; 602.143;MV |
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Admin @ si @ EPW2016 |
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2827 |
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Author |
Marc Bolaños; Petia Radeva |
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Title |
Simultaneous Food Localization and Recognition |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition |
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CoRR abs/1604.07953
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays – object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Cancun; Mexico; December 2016 |
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MILAB; no proj |
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Admin @ si @ BoR2016 |
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2834 |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Title |
With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams |
Type |
Conference Article |
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2016 |
Publication |
23rd International Conference on Pattern Recognition |
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Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Cancun; Mexico; December 2016 |
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MILAB |
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Admin @ si @ ADR2016d |
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2835 |
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Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Fusion of Classifier Predictions for Audio-Visual Emotion Recognition |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition Workshops |
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In this paper is presented a novel multimodal emotion recognition system which is based on the analysis of audio and visual cues. MFCC-based features are extracted from the audio channel and facial landmark geometric relations are
computed from visual data. Both sets of features are learnt separately using state-of-the-art classifiers. In addition, we summarise each emotion video into a reduced set of key-frames, which are learnt in order to visually discriminate emotions by means of a Convolutional Neural Network. Finally, confidence
outputs of all classifiers from all modalities are used to define a new feature space to be learnt for final emotion prediction, in a late fusion/stacking fashion. The conducted experiments on eNTERFACE’05 database show significant performance improvements of our proposed system in comparison to state-of-the-art approaches. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Cancun; Mexico; December 2016 |
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HuPBA;MILAB; |
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Admin @ si @ NMN2016 |
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2839 |
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Anjan Dutta; Umapada Pal; Josep Llados |
![goto web page url](img/www.gif)
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Title |
Compact Correlated Features for Writer Independent Signature Verification |
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2016 |
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23rd International Conference on Pattern Recognition |
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This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Cancun; Mexico; December 2016 |
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DAG; 600.097 |
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Admin @ si @ DPL2016 |
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2875 |
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Marco Bellantonio; Mohammad A. Haque; Pau Rodriguez; Kamal Nasrollahi; Taisi Telve; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund; Pejman Rasti; Golamreza Anbarjafari |
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Title |
Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition |
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10165 |
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Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Cancun; Mexico; December 2016 |
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HuPBA; ISE; 600.098; 600.119 |
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Admin @ si @ BHR2016 |
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2902 |
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Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Human Head Pose Estimation on SASE database using Random Hough Regression Forests |
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2016 |
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23rd International Conference on Pattern Recognition Workshops |
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10165 |
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In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Cancun; Mexico; December 2016 |
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HuPBA; |
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Admin @ si @ LEA2016b |
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2910 |
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Jose Ramirez Moreno; Juan R Revilla; Miguel Reyes; Sergio Escalera |
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Title |
Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal |
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2016 |
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4th Congreso WCPT-SAR |
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Buenos Aires; Argentina; June 2016 |
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WCPT-SAR |
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HuPBA;MILAB |
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Admin @ si @ RRR2016 |
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2853 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Giving Value to digital collections in the Public Library |
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2016 |
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Librarian 2020 |
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Brussels; Belgium; October 2016 |
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LIB |
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MV; 600.097;SIAI |
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no |
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Admin @ si @Vil2016a |
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2802 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A Living Lab approach for Citizen Science in Libraries |
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2016 |
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1st International ECSA Conference |
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Berlin; Germany; May 2016 |
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ECSA |
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MV; DAG; 600.084; 600.097;SIAI |
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no |
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Admin @ si @ViK2016 |
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2804 |
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Author |
Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Does Multimodality Help Human and Machine for Translation and Image Captioning? |
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Conference Article |
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2016 |
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1st conference on machine translation |
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This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR. |
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Berlin; Germany; August 2016 |
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WMT |
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LAMP; 600.106 ; 600.068 |
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no |
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Admin @ si @ CAW2016 |
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2761 |
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