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Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera |
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Title |
The AutoML challenge on codalab |
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2015 |
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IEEE International Joint Conference on Neural Networks IJCNN2015 |
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Killarney; Ireland; July 2015 |
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IJCNN |
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HuPBA;MILAB |
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no |
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Admin @ si @ GBC2015b |
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2650 |
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Author |
Gerard Canal; Cecilio Angulo; Sergio Escalera |
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Gesture based Human Multi-Robot interaction |
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2015 |
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IEEE International Joint Conference on Neural Networks IJCNN2015 |
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The emergence of robot applications for nontechnical users implies designing new ways of interaction between robotic platforms and users. The main goal of this work is the development of a gestural interface to interact with robots
in a similar way as humans do, allowing the user to provide information of the task with non-verbal communication. The gesture recognition application has been implemented using the Microsoft’s KinectTM v2 sensor. Hence, a real-time algorithm based on skeletal features is described to deal with both, static
gestures and dynamic ones, being the latter recognized using a weighted Dynamic Time Warping method. The gesture recognition application has been implemented in a multi-robot case.
A NAO humanoid robot is in charge of interacting with the users and respond to the visual signals they produce. Moreover, a wheeled Wifibot robot carries both the sensor and the NAO robot, easing navigation when necessary. A broad set of user tests have been carried out demonstrating that the system is, indeed, a
natural approach to human robot interaction, with a fast response and easy to use, showing high gesture recognition rates. |
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Killarney; Ireland; July 2015 |
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IJCNN |
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HuPBA;MILAB |
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CAE2015a |
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2651 |
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Olivier Lefebvre; Pau Riba; Charles Fournier; Alicia Fornes; Josep Llados; Rejean Plamondon; Jules Gagnon-Marchand |
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Monitoring neuromotricity on-line: a cloud computing approach |
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2015 |
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17th Conference of the International Graphonomics Society IGS2015 |
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The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain. |
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Pointe-à-Pitre; Guadeloupe; June 2015 |
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IGS |
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DAG; 600.077 |
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Admin @ si @ LRF2015 |
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2617 |
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Author |
M. Campos-Taberner; Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
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Title |
Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination |
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2015 |
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IEEE International Geoscience and Remote Sensing Symposium IGARSS2015 |
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4169 - 4172 |
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This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization. |
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Milan; Italy; July 2015 |
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IGARSS |
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LAMP; 600.079;MILAB |
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Admin @ si @ CRG2015 |
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2724 |
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J.Kuhn; A.Nussbaumer; J.Pirker; Dimosthenis Karatzas; A. Pagani; O.Conlan; M.Memmel; C.M.Steiner; C.Gutl; D.Albert; Andreas Dengel |
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Title |
Advancing Physics Learning Through Traversing a Multi-Modal Experimentation Space |
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2015 |
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Workshop Proceedings on the 11th International Conference on Intelligent Environments |
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19 |
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373-380 |
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Translating conceptual knowledge into real world experiences presents a significant educational challenge. This position paper presents an approach that supports learners in moving seamlessly between conceptual learning and their application in the real world by bringing physical and virtual experiments into everyday settings. Learners are empowered in conducting these situated experiments in a variety of physical settings by leveraging state of the art mobile, augmented reality, and virtual reality technology. A blend of mobile-based multi-sensory physical experiments, augmented reality and enabling virtual environments can allow learners to bridge their conceptual learning with tangible experiences in a completely novel manner. This approach focuses on the learner by applying self-regulated personalised learning techniques, underpinned by innovative pedagogical approaches and adaptation techniques, to ensure that the needs and preferences of each learner are catered for individually. |
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Praga; Chzech Republic; July 2015 |
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IE |
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DAG; 600.077 |
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Admin @ si @ KNP2015 |
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2694 |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
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Title |
Towards social interaction detection in egocentric photo-streams |
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2015 |
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Proceedings of SPIE, 8th International Conference on Machine Vision , ICMV 2015 |
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9875 |
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Detecting social interaction in videos relying solely on visual cues is a valuable task that is receiving increasing attention in recent years. In this work, we address this problem in the challenging domain of egocentric photo-streams captured by a low temporal resolution wearable camera (2fpm). The major difficulties to be handled in this context are the sparsity of observations as well as unpredictability of camera motion and attention orientation due to the fact that the camera is worn as part of clothing. Our method consists of four steps: multi-faces localization and tracking, 3D localization, pose estimation and analysis of f-formations. By estimating pair-to-pair interaction probabilities over the sequence, our method states the presence or absence of interaction with the camera wearer and specifies which people are more involved in the interaction. We tested our method over a dataset of 18.000 images and we show its reliability on our considered purpose. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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ICMV |
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MILAB |
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Admin @ si @ ADR2015a |
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2702 |
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Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Mehreen Saeed; Alexander Statnikov; Evelyne Viegas |
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Title |
AutoML Challenge 2015: Design and First Results |
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2015 |
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32nd International Conference on Machine Learning, ICML workshop, JMLR proceedings ICML15 |
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1-8 |
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AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning |
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ChaLearn is organizing the Automatic Machine Learning (AutoML) contest 2015, which challenges participants to solve classi cation and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing diculty are introduced throughout the six rounds of the challenge. (Participants can
enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen (AutoML), and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance (Tweakathon). This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML. |
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Lille; France; July 2015 |
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ICML |
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HuPBA;MILAB |
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Admin @ si @ GBC2015c |
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2656 |
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Author |
Marc Bolaños; R. Mestre; Estefania Talavera; Xavier Giro; Petia Radeva |
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Title |
Visual Summary of Egocentric Photostreams by Representative Keyframes |
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2015 |
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IEEE International Conference on Multimedia and Expo ICMEW2015 |
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1-6 |
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egocentric; lifelogging; summarization; keyframes |
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Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries. |
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Torino; italy; July 2015 |
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978-1-4799-7079-7 |
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978-1-4799-7079-7 |
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ICME |
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MILAB |
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no |
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Admin @ si @ BMT2015 |
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2638 |
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Adriana Romero; Nicolas Ballas; Samira Ebrahimi Kahou; Antoine Chassang; Carlo Gatta; Yoshua Bengio |
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Title |
FitNets: Hints for Thin Deep Nets |
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2015 |
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3rd International Conference on Learning Representations ICLR2015 |
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Computer Science ; Learning; Computer Science ;Neural and Evolutionary Computing |
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While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network. |
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San Diego; CA; May 2015 |
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ICLR |
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MILAB |
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no |
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Admin @ si @ RBK2015 |
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2593 |
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Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo |
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LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations |
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2015 |
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22th IEEE International Conference on Image Processing |
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178 - 181 |
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Quebec; Canada; September 2015 |
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ICIP |
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ADAS; 600.076 |
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Admin @ si @ AST2015 |
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2630 |
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Nuria Cirera; Alicia Fornes; Josep Llados |
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Hidden Markov model topology optimization for handwriting recognition |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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626-630 |
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In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ CFL2015 |
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2639 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Handwritten Word Spotting by Inexact Matching of Grapheme Graphs |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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781 - 785 |
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This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections. |
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ICDAR |
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DAG; 600.077; 600.061; 602.006 |
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Admin @ si @ RLF2015b |
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2642 |
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Jean-Christophe Burie; J. Chazalon; M. Coustaty; S. Eskenazi; Muhammad Muzzamil Luqman; M. Mehri; Nibal Nayef; Jean-Marc Ogier; S. Prum; Marçal Rusiñol |
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ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc) |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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1161 - 1165 |
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Smartphones are enabling new ways of capture,
hence arises the need for seamless and reliable acquisition and
digitization of documents, in order to convert them to editable,
searchable and a more human-readable format. Current stateof-the-art
works lack databases and baseline benchmarks for
digitizing mobile captured documents. We have organized a
competition for mobile document capture and OCR in order to
address this issue. The competition is structured into two independent
challenges: smartphone document capture, and smartphone
OCR. This report describes the datasets for both challenges
along with their ground truth, details the performance evaluation
protocols which we used, and presents the final results of the
participating methods. In total, we received 13 submissions: 8
for challenge-I, and 5 for challenge-2. |
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Nancy; France; August 2015 |
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DAG; 600.077; 601.223; 600.084 |
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Admin @ si @ BCC2015 |
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2681 |
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Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
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Towards Query-by-Speech Handwritten Keyword Spotting |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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501-505 |
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In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset. |
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Nancy; France; August 2015 |
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DAG; 600.084; 600.061; 601.223; 600.077;ADAS |
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Admin @ si @ RAT2015b |
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2682 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados; R.Jain; D.Doermann |
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Novel Line Verification for Multiple Instance Focused Retrieval in Document Collections |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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481-485 |
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Nancy; France; August 2015 |
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DAG; 600.077; 601.223; 600.084; 600.061 |
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Admin @ si @ GRK2015 |
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2683 |
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