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Author |
Volkmar Frinken; Andreas Fischer; Carlos David Martinez Hinarejos |
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Title |
Handwriting Recognition in Historical Documents using Very Large Vocabularies |
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Conference Article |
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Year |
2013 |
Publication  |
2nd International Workshop on Historical Document Imaging and Processing |
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Pages |
67-72 |
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Abstract |
Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words. |
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Washington; USA; August 2013 |
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978-1-4503-2115-0 |
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HIP |
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Notes |
DAG; 600.056; 600.045; 600.061; 602.006; 602.101 |
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no |
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Call Number |
Admin @ si @ FFM2013 |
Serial |
2296 |
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Author |
Alex Pardo; Albert Clapes; Sergio Escalera; Oriol Pujol |
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Title |
Actions in Context: System for people with Dementia |
Type |
Conference Article |
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Year |
2013 |
Publication |
2nd International Workshop on Citizen Sensor Networks (Citisen2013) at the European Conference on Complex Systems |
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Pages |
3-14 |
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Keywords |
Multi-modal data Fusion; Computer vision; Wearable sensors; Gesture recognition; Dementia |
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Abstract |
In the next forty years, the number of people living with dementia is expected to triple. In the last stages, people affected by this disease become dependent. This hinders the autonomy of the patient and has a huge social impact in time, money and effort. Given this scenario, we propose an ubiquitous system capable of recognizing daily specific actions. The system fuses and synchronizes data obtained from two complementary modalities – ambient and egocentric. The ambient approach consists in a fixed RGB-Depth camera for user and object recognition and user-object interaction, whereas the egocentric point of view is given by a personal area network (PAN) formed by a few wearable sensors and a smartphone, used for gesture recognition. The system processes multi-modal data in real-time, performing paralleled task recognition and modality synchronization, showing high performance recognizing subjects, objects, and interactions, showing its reliability to be applied in real case scenarios. |
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Barcelona; September 2013 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-04177-3 |
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ECCS |
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HUPBA;MILAB |
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no |
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Call Number |
Admin @ si @ PCE2013 |
Serial |
2354 |
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Author |
M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Title |
Cross-spectral image registration and fusion: an evaluation study |
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Conference Article |
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Year |
2015 |
Publication |
2nd International Conference on Machine Vision and Machine Learning |
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Keywords |
multispectral imaging; image registration; data fusion; infrared and visible spectra |
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Abstract |
This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Barcelona; July 2015 |
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MVML |
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Notes |
ADAS; 600.076 |
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no |
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Call Number |
Admin @ si @ CAV2015 |
Serial |
2629 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Traffic Sign Classification using Error Correcting Techniques |
Type |
Conference Article |
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Year |
2007 |
Publication |
2nd International Conference on Computer Vision Theory and Applications |
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281–285 |
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Barcelona (Spain) |
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VISAPP |
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Notes |
MILAB;HuPBA |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2007a |
Serial |
909 |
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Author |
David Masip; Agata Lapedriza; Jordi Vitria |
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Title |
Face Verification Sharing Knowledge from Different Subjects |
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Conference Article |
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Year |
2007 |
Publication |
2nd International Conference on Computer Vision Theory and Applications |
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2 |
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Pages |
268–289 |
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Address |
Barcelona (Spain) |
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VISAPP´07 |
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Notes |
OR; MV |
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no |
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Call Number |
BCNPCL @ bcnpcl @ MLV2007a |
Serial |
995 |
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Author |
Partha Pratim Roy; Josep Llados |
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Title |
Multi-Oriented Character Recognition from Graphical Documents |
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Conference Article |
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Year |
2008 |
Publication |
2nd International Conference on Cognition and Recognition |
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30–35 |
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Address |
Mandya (India) |
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ICCR |
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DAG |
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no |
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Call Number |
DAG @ dag @ RLP2008 |
Serial |
965 |
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Author |
Ariel Amato; Angel Sappa; Alicia Fornes; Felipe Lumbreras; Josep Llados |
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Title |
Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform |
Type |
Conference Article |
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Year |
2013 |
Publication |
2nd International ACM Workshop on Crowdsourcing for Multimedia |
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Pages |
21-22 |
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Abstract |
In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized. |
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Barcelona; October 2013 |
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ISBN |
978-1-4503-2396-3 |
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CrowdMM |
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Notes |
ADAS; ISE; DAG; 600.054; 600.055; 600.045; 600.061; 602.006 |
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no |
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Call Number |
Admin @ si @ SLA2013 |
Serial |
2335 |
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Author |
Sergio Escalera; Eloi Puertas; Petia Radeva; Oriol Pujol |
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Title |
Multimodal laughter recognition in video conversations |
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Conference Article |
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Year |
2009 |
Publication |
2nd IEEE Workshop on CVPR for Human communicative Behavior analysis |
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110–115 |
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Abstract |
Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper, we propose a multi-modal methodology based on the fusion of audio and visual cues to deal with the laughter recognition problem in face-to-face conversations. The audio features are extracted from the spectogram and the video features are obtained estimating the mouth movement degree and using a smile and laughter classifier. Finally, the multi-modal cues are included in a sequential classifier. Results over videos from the public discussion blog of the New York Times show that both types of features perform better when considered together by the classifier. Moreover, the sequential methodology shows to significantly outperform the results obtained by an Adaboost classifier. |
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Address |
Miami (USA) |
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2160-7508 |
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978-1-4244-3994-2 |
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CVPR |
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Notes |
MILAB;HuPBA |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2009c |
Serial |
1188 |
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Author |
Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera |
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Title |
Dominance Detection in Face-to-face Conversations |
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Conference Article |
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2009 |
Publication |
2nd IEEE Workshop on CVPR for Human communicative Behavior analysis |
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97–102 |
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Dominance is referred to the level of influence a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on dominance detection from visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers opinion. Moreover, the considered indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analysis shows a high correlation and allows the categorization of dominant people in public discussion video sequences. |
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Miami, USA |
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2160-7508 |
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978-1-4244-3994-2 |
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CVPR |
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HuPBA; OR; MILAB;MV |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EMV2009 |
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1227 |
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Author |
Gemma Roig; Xavier Boix; Fernando De la Torre |
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Title |
Optimal Feature Selection for Subspace Image Matching |
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Conference Article |
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Year |
2009 |
Publication |
2nd IEEE International Workshop on Subspace Methods in conjunction |
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Image matching has been a central research topic in computer vision over the last decades. Typical approaches to correspondence involve matching feature points between images. In this paper, we present a novel problem for establishing correspondences between a sparse set of image features and a previously learned subspace model. We formulate the matching task as an energy minimization, and jointly optimize over all possible feature assignments and parameters of the subspace model. This problem is in general NP-hard. We propose a convex relaxation approximation, and develop two optimization strategies: naïve gradient-descent and quadratic programming. Alternatively, we reformulate the optimization criterion as a sparse eigenvalue problem, and solve it using a recently proposed backward greedy algorithm. Experimental results on facial feature detection show that the quadratic programming solution provides better selection mechanism for relevant features. |
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Kyoto, Japan |
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ICCV |
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no |
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Admin @ si @ RBT2009 |
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1233 |
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Author |
Jürgen Brauer; Wenjuan Gong; Jordi Gonzalez; Michael Arens |
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Title |
On the Effect of Temporal Information on Monocular 3D Human Pose Estimation |
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Conference Article |
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2011 |
Publication |
2nd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams |
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906 - 913 |
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We address the task of estimating 3D human poses from monocular camera sequences. Many works make use of multiple consecutive frames for the estimation of a 3D pose in a frame. Although such an approach should ease the pose estimation task substantially since multiple consecutive frames allow to solve for 2D projection ambiguities in principle, it has not yet been investigated systematically how much we can improve the 3D pose estimates when using multiple consecutive frames opposed to single frame information. In this paper we analyze the difference in quality of 3D pose estimates based on different numbers of consecutive frames from which 2D pose estimates are available. We validate the use of temporal information on two major different approaches for human pose estimation – modeling and learning approaches. The results of our experiments show that both learning and modeling approaches benefit from using multiple frames opposed to single frame input but that the benefit is small when the 2D pose estimates show a high quality in terms of precision. |
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Barcelona |
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978-1-4673-0062-9 |
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ARTEMIS |
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ISE |
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Admin @ si @BGG 2011 |
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1860 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Appearance-based 3D Face Tracker: An Evaluation Study |
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Miscellaneous |
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2005 |
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2nd IEEE Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 121–128 |
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Beijing (China) |
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ADAS |
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ADAS @ adas @ DoS2005b |
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580 |
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Author |
Gemma Sanchez; Josep Llados; Enric Marti |
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A string-based method to recognize symbols and structural textures in architectural plans |
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1997 |
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2nd IAPR Workshop on Graphics Recognition |
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This paper deals with the recognition of symbols and struc- tural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clus- tering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the simila- rity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. |
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Nancy, France |
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DAG; IAM |
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IAM @ iam @ SLE1997 |
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1498 |
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Author |
Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer |
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Title |
LIUM-CVC Submissions for WMT17 Multimodal Translation Task |
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Conference Article |
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2017 |
Publication |
2nd Conference on Machine Translation |
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This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU. |
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LAMP; 600.106; 600.120 |
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Admin @ si @ CAB2017 |
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3035 |
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Albin Soutif; Antonio Carta; Joost Van de Weijer |
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Improving Online Continual Learning Performance and Stability with Temporal Ensembles |
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2023 |
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2nd Conference on Lifelong Learning Agents |
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Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al., 2022; Lange et al., 2023) arXiv:2205.13452 showed that replay methods used in continual learning suffer from the stability gap, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature. |
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Montreal; Canada; August 2023 |
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Admin @ si @ SCW2023 |
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3922 |
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