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Sergio Escalera; Jordi Gonzalez; Xavier Baro; Pablo Pardo; Junior Fabian; Marc Oliu; Hugo Jair Escalante; Ivan Huerta; Isabelle Guyon |
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Title |
ChaLearn Looking at People 2015 new competitions: Age Estimation and Cultural Event Recognition |
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Conference Article |
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Year |
2015 |
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IEEE International Joint Conference on Neural Networks IJCNN2015 |
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1-8 |
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Following previous series on Looking at People (LAP) challenges [1], [2], [3], in 2015 ChaLearn runs two new competitions within the field of Looking at People: age and cultural event recognition in still images. We propose thefirst crowdsourcing application to collect and label data about apparent
age of people instead of the real age. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes both challenges and data, providing some initial baselines. The results of the first round of the competition were presented at ChaLearn LAP 2015 IJCNN special session on computer vision and robotics http://www.dtic.ua.es/∼jgarcia/IJCNN2015.
Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. |
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Killarney; Ireland; July 2015 |
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IJCNN |
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HuPBA; ISE; 600.063; 600.078;MV |
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no |
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Admin @ si @ EGB2015 |
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2591 |
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Author |
Sergio Escalera; Jordi Gonzalez; Xavier Baro; Miguel Reyes; Oscar Lopes; Isabelle Guyon; V. Athitsos; Hugo Jair Escalante |
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Title |
Multi-modal Gesture Recognition Challenge 2013: Dataset and Results |
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Conference Article |
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Year |
2013 |
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15th ACM International Conference on Multimodal Interaction |
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445-452 |
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The recognition of continuous natural gestures is a complex and challenging problem due to the multi-modal nature of involved visual cues (e.g. fingers and lips movements, subtle facial expressions, body pose, etc.), as well as technical limitations such as spatial and temporal resolution and unreliable
depth cues. In order to promote the research advance on this field, we organized a challenge on multi-modal gesture recognition. We made available a large video database of 13; 858 gestures from a lexicon of 20 Italian gesture categories recorded with a KinectTM camera, providing the audio, skeletal model, user mask, RGB and depth images. The focus of the challenge was on user independent multiple gesture learning. There are no resting positions and the gestures are performed in continuous sequences lasting 1-2 minutes, containing between 8 and 20 gesture instances in each sequence. As a result, the dataset contains around 1:720:800 frames. In addition to the 20 main gesture categories, ‘distracter’ gestures are included, meaning that additional audio
and gestures out of the vocabulary are included. The final evaluation of the challenge was defined in terms of the Levenshtein edit distance, where the goal was to indicate the real order of gestures within the sequence. 54 international teams participated in the challenge, and outstanding results
were obtained by the first ranked participants. |
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Sidney; Australia; December 2013 |
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978-1-4503-2129-7 |
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ICMI |
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HUPBA; ISE; 600.063;MV |
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no |
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Admin @ si @ EGB2013 |
Serial |
2373 |
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Sergio Escalera; Jordi Gonzalez; Xavier Baro; Fernando Alonso; Martha Mackay |
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Title |
Care Respite: a remote monitoring eHealth system for improving ambient assisted living |
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Conference Article |
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Year |
2016 |
Publication |
Human Motion Analysis for Healthcare Applications |
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Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.
In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions.
This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations. |
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Savoy Place; London; uk; May 2016 |
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HMAHA |
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HuPBA; ISE; |
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no |
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Admin @ si @ EGB2016 |
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2852 |
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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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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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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; Alicia Fornes; Oriol Pujol; Petia Radeva |
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Title |
Multi-class Binary Symbol Classification with Circular Blurred Shape Models |
Type |
Conference Article |
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Year |
2009 |
Publication |
15th International Conference on Image Analysis and Processing |
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Volume |
5716 |
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Pages |
1005–1014 |
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Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements. |
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Salerno, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04145-7 |
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ICIAP |
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MILAB;HuPBA;DAG |
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no |
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BCNPCL @ bcnpcl @ EFP2009c |
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1186 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
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Title |
Multi-class Binary Object Categorization using Blurred Shape Models |
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Conference Article |
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2007 |
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Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern |
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4756 |
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773–782 |
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978-3-540-76724-4 |
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CIARP |
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MILAB; DAG;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EFP2007 |
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911 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Alberto Escudero; Petia Radeva |
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Title |
Circular Blurred Shape Model for Symbol Spotting in Documents |
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Conference Article |
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2009 |
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16th IEEE International Conference on Image Processing |
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1985-1988 |
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Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors. |
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Cairo, Egypt |
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978-1-4244-5653-6 |
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ICIP |
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MILAB;HuPBA;DAG |
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no |
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BCNPCL @ bcnpcl @ EFP2009b |
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1184 |
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Sergio Escalera |
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Human Behavior Analysis From Depth Maps |
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Conference Article |
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2012 |
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7th Conference on Articulated Motion and Deformable Objects |
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7378 |
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282-292 |
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Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect \textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed. |
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Mallorca |
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Springer Heidelberg |
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F.J. Perales; R.B. Fisher; T.B. Moeslund |
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0302-9743 |
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978-3-642-31566-4 |
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AMDO |
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MILAB; HuPBA |
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no |
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Admin @ si @ Esc2012 |
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2040 |
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Sergio Alloza; Flavio Escribano; Sergi Delgado; Ciprian Corneanu; Sergio Escalera |
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XBadges. Identifying and training soft skills with commercial video games Improving persistence, risk taking & spatial reasoning with commercial video games and facial and emotional recognition system |
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Conference Article |
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Year |
2017 |
Publication |
4th Congreso de la Sociedad Española para las Ciencias del Videojuego |
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1957 |
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13-28 |
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Video Games; Soft Skills; Training; Skilling Development; Emotions; Cognitive Abilities; Flappy Bird; Pacman; Tetris |
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XBadges is a research project based on the hypothesis that commercial video games (nonserious games) can train soft skills. We measure persistence, patial reasoning and risk taking before and after subjects paticipate in controlled game playing sessions.
In addition, we have developed an automatic facial expression recognition system capable of inferring their emotions while playing, allowing us to study the role of emotions in soft skills acquisition. We have used Flappy Bird, Pacman and Tetris for assessing changes in persistence, risk taking and spatial reasoning respectively.
Results show how playing Tetris significantly improves spatial reasoning and how playing Pacman significantly improves prudence in certain areas of behavior. As for emotions, they reveal that being concentrated helps to improve performance and skills acquisition. Frustration is also shown as a key element. With the results obtained we are able to glimpse multiple applications in areas which need soft skills development. |
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Barcelona; June 2017 |
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COSECIVI; CEUR-WS |
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HUPBA; no menciona |
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no |
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Admin @ si @ AED2017 |
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3065 |
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Author |
Sergi Garcia Bordils; George Tom; Sangeeth Reddy; Minesh Mathew; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas |
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Read While You Drive-Multilingual Text Tracking on the Road |
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Conference Article |
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2022 |
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15th IAPR International workshop on document analysis systems |
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13237 |
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756–770 |
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Visual data obtained during driving scenarios usually contain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully annotated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods exploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild. |
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La Rochelle; France; May 2022 |
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978-3-031-06554-5 |
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DAS |
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DAG; 600.155; 611.022; 611.004 |
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no |
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Admin @ si @ GTR2022 |
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3783 |
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Author |
Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol |
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Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning |
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Conference Article |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14192 |
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106-121 |
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Scene Text Detection; Scene Text Recognition; Transformer Acceleration |
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Scene text detection and recognition is a crucial task in computer vision with numerous real-world applications. Transformer-based approaches are behind all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training and inference times without sacrificing performance, striking a balance between accuracy and speed. We have applied this pruning technique to our own end-to-end transformer-based scene text understanding architecture. Our method uses a separate detection branch to guide the pruning of uninformative image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ GKR2023a |
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3907 |
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Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol |
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Title |
STEP – Towards Structured Scene-Text Spotting |
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Conference Article |
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2024 |
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Winter Conference on Applications of Computer Vision |
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883-892 |
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We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios. |
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Waikoloa; Hawai; USA; January 2024 |
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WACV |
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DAG |
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no |
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Admin @ si @ GKR2024 |
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3992 |
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Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas |
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Out-of-Vocabulary Challenge Report |
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Conference Article |
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2022 |
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Proceedings European Conference on Computer Vision Workshops |
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13804 |
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359–375 |
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This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. |
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Tel-Aviv; Israel; October 2022 |
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ECCVW |
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DAG; 600.155; 302.105; 611.002 |
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Admin @ si @ GMB2022 |
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3771 |
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Author |
Senmao Li; Joost Van de Weijer; Yaxing Wang; Fahad Shahbaz Khan; Meiqin Liu; Jian Yang |
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Title |
3D-Aware Multi-Class Image-to-Image Translation with NeRFs |
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Conference Article |
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2023 |
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36th IEEE Conference on Computer Vision and Pattern Recognition |
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12652-12662 |
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Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multiclass image-to-image (3D-aware 121) translation. Naively using 2D-121 translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multiclass 121 translation, we decouple this learning process into a multiclass 3D-aware GAN step and a 3D-aware 121 translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multiclass 3D-aware GAN architecture, that preserves view-consistency, we construct a 3D-aware 121 translation system. To further reduce the view-consistency problems, we propose several new techniques, including a U-net-like adaptor network design, a hierarchical representation constrain and a relative regularization loss. In exten-sive experiments on two datasets, quantitative and qualitative results demonstrate that we successfully perform 3D-aware 121 translation with multi-view consistency. Code is available in 3DI2I. |
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Vancouver; Canada; June 2023 |
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CVPR |
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LAMP |
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Admin @ si @ LWW2023b |
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3920 |
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Sebastien Mace; Herve Locteau; Ernest Valveny; Salvatore Tabbone |
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A system to detect rooms in architectural floor plan images |
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2010 |
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9th IAPR International Workshop on Document Analysis Systems |
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167–174 |
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In this article, a system to detect rooms in architectural floor plan images is described. We first present a primitive extraction algorithm for line detection. It is based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection. We show how the lines that satisfy some graphical arrangements are combined into walls. We also present the way we detect some door hypothesis thanks to the extraction of arcs. Walls and door hypothesis are then used by our room segmentation strategy; it consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines between regions can also be rough. We take advantage of knowledge associated to architectural floor plans in order to obtain mostly rectangular rooms. Qualitative and quantitative evaluations performed on a corpus of real documents show promising results. |
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Boston; USA |
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978-1-60558-773-8 |
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DAS |
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DAG |
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no |
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DAG @ dag @ MLV2010 |
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1437 |
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