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Author |
Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio |
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Title |
Introduction to NIPS 2017 Competition Track |
Type |
Book Chapter |
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Year |
2018 |
Publication |
The NIPS ’17 Competition: Building Intelligent Systems |
Abbreviated Journal |
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Pages |
1-23 |
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Abstract |
Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?
In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter. |
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Springer |
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Editor |
Sergio Escalera; Markus Weimer |
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978-3-319-94042-7 |
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Notes |
HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ EWB2018 |
Serial |
3200 |
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Permanent link to this record |
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Author |
Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez |
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Title |
Top-down model fitting for hand pose recovery in sequences of depth images |
Type |
Journal Article |
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Year |
2018 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
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Volume |
79 |
Issue |
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Pages |
63-75 |
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Abstract |
State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs. |
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Notes |
HUPBA; 600.098 |
Approved |
no |
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Call Number |
Admin @ si @ MEC2018 |
Serial |
3203 |
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Author |
Marc Oliu; Javier Selva; Sergio Escalera |
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Title |
Folded Recurrent Neural Networks for Future Video Prediction |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
11218 |
Issue |
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Pages |
745-761 |
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Abstract |
Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach. |
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Munich; September 2018 |
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LNCS |
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ECCV |
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Notes |
HUPBA; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ OSE2018 |
Serial |
3204 |
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Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera |
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Title |
Deep Structure Inference Network for Facial Action Unit Recognition |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
11216 |
Issue |
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Pages |
309-324 |
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Keywords |
Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference |
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Abstract |
Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for general facial expression analysis. Recently, efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between AUs. We propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively. |
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Munich; September 2018 |
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ECCV |
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Notes |
HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ CME2018 |
Serial |
3205 |
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Author |
Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro |
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Title |
Residual Stacked RNNs for Action Recognition |
Type |
Conference Article |
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Year |
2018 |
Publication |
9th International Workshop on Human Behavior Understanding |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
534-548 |
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Keywords |
Action recognition; Deep residual learning; Two-stream RNN |
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Abstract |
Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset. |
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Address |
Munich; September 2018 |
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ECCVW |
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Notes |
HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ LCE2018b |
Serial |
3206 |
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Author |
Cristina Palmero; Javier Selva; Mohammad Ali Bagheri; Sergio Escalera |
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Title |
Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues |
Type |
Conference Article |
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Year |
2018 |
Publication |
29th British Machine Vision Conference |
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Abstract |
Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included. |
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Address |
Newcastle; UK; September 2018 |
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BMVC |
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Notes |
HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ PSB2018 |
Serial |
3208 |
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Permanent link to this record |
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Author |
Gabriela Ramirez; Esau Villatoro; Bogdan Ionescu; Hugo Jair Escalante; Sergio Escalera; Martha Larson; Henning Muller; Isabelle Guyon |
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Title |
Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contes |
Type |
Conference Article |
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Year |
2018 |
Publication |
Multimedia Information Processing for Personality and Social Networks Analysis (MIPPSNA 2018) |
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Address |
Beijing; China; August 2018 |
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ICPRW |
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Notes |
HUPBA |
Approved |
no |
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Call Number |
Admin @ si @ RVI2018 |
Serial |
3211 |
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Permanent link to this record |
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Author |
Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Review on Emotion Recognition Databases |
Type |
Book Chapter |
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Year |
2018 |
Publication |
Human-Robot Interaction: Theory and Application |
Abbreviated Journal |
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Keywords |
emotion; computer vision; databases |
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Abstract |
Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. |
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ISBN |
978-1-78923-316-2 |
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Notes |
HUPBA; 602.133 |
Approved |
no |
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Call Number |
Admin @ si @ HRL2018 |
Serial |
3212 |
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Author |
Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya |
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Title |
Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” |
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Journal |
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Year |
2018 |
Publication |
Informaciones Psiquiatricas |
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232 |
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Pages |
47-59 |
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0210-7279 |
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Notes |
HUPBA; no menciona |
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no |
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Call Number |
Admin @ si @ FAA2018 |
Serial |
3214 |
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Author |
Suman Ghosh |
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Title |
Word Spotting and Recognition in Images from Heterogeneous Sources A |
Type |
Book Whole |
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Year |
2018 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Text is the most common way of information sharing from ages. With recent development of personal images databases and handwritten historic manuscripts the demand for algorithms to make these databases accessible for browsing and indexing are in rise. Enabling search or understanding large collection of manuscripts or image databases needs fast and robust methods. Researchers have found different ways to represent cropped words for understanding and matching, which works well when words are already segmented. However there is no trivial way to extend these for non-segmented documents. In this thesis we explore different methods for text retrieval and recognition from unsegmented document and scene images. Two different ways of representation exist in literature, one uses a fixed length representation learned from cropped words and another a sequence of features of variable length. Throughout this thesis, we have studied both these representation for their suitability in segmentation free understanding of text. In the first part we are focused on segmentation free word spotting using a fixed length representation. We extended the use of the successful PHOC (Pyramidal Histogram of Character) representation to segmentation free retrieval. In the second part of the thesis, we explore sequence based features and finally, we propose a unified solution where the same framework can generate both kind of representations. |
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November 2018 |
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Thesis |
Ph.D. thesis |
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Ediciones Graficas Rey |
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Editor |
Ernest Valveny |
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978-84-948531-0-4 |
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Notes |
DAG; 600.121 |
Approved |
no |
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Call Number |
Admin @ si @ Gho2018 |
Serial |
3217 |
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Permanent link to this record |
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Author |
Gholamreza Anbarjafari; Sergio Escalera |
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Title |
Human-Robot Interaction: Theory and Application |
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Book Whole |
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2018 |
Publication |
Human-Robot Interaction: Theory and Application |
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978-1-78923-316-2 |
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HUPBA |
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no |
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Admin @ si @ AnE2018 |
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3216 |
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Author |
Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi |
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Title |
WiCV 2018: The Fourth Women In Computer Vision Workshop |
Type |
Conference Article |
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Year |
2018 |
Publication |
4th Women in Computer Vision Workshop |
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1941-19412 |
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Keywords |
Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning |
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Abstract |
We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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Salt Lake City; USA; June 2018 |
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WiCV |
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Notes |
DAG; 600.121; 600.129 |
Approved |
no |
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Call Number |
Admin @ si @ DBR2018 |
Serial |
3222 |
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Author |
Arnau Baro; Pau Riba; Alicia Fornes |
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Title |
A Starting Point for Handwritten Music Recognition |
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Conference Article |
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Year |
2018 |
Publication |
1st International Workshop on Reading Music Systems |
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5-6 |
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Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA |
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Abstract |
In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. |
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Paris; France; September 2018 |
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WORMS |
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Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
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no |
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Admin @ si @ BRF2018 |
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3223 |
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Laura Lopez-Fuentes; Alessandro Farasin; Harald Skinnemoen; Paolo Garza |
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Title |
Deep Learning models for passability detection of flooded roads |
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Conference Article |
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2018 |
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MediaEval 2018 Multimedia Benchmark Workshop |
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2283 |
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In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task. |
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Sophia Antipolis; France; October 2018 |
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MediaEval |
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LAMP; 600.084; 600.109; 600.120 |
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no |
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Admin @ si @ LFS2018 |
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3224 |
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Author |
Anjan Dutta; Hichem Sahbi |
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Title |
Stochastic Graphlet Embedding |
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Journal Article |
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Year |
2018 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
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TNNLS |
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1-14 |
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Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality |
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Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider
these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When
combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases. |
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DAG; 602.167; 602.168; 600.097; 600.121 |
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Admin @ si @ DuS2018 |
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3225 |
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