@article {aGeleta, title = {Deep Variational Autoencoders for Population Genetics}, year = {Submitted}, abstract = {

Motivation Modern biobanks provide numerous high-resolution genomic sequences of diverse populations. These datasets enable a better understanding of genotype-phenotype interactions with genome-wide association studies (GWAS) and power a new personalized precision medicine with polygenic risk scores (PRS). In order to account for diverse and admixed populations, new algorithmic tools are needed in order to properly capture the genetic composition of populations. Here we explore deep learning techniques, namely variational autoencoders (VAEs), to process genomic data from a population perspective. We hope this work will encourage the adoption of deep neural networks in the population genetics community.

Results In this paper, we show the power of VAEs for a variety of tasks relating to the interpretation, classification, simulation, and compression of genomic data with several worldwide whole genome datasets from both humans and canids and evaluate the performance of the proposed applications with and without ancestry conditioning. The unsupervised setting of autoencoders allows for the detection and learning of granular population structure and inferring of informative latent factors. The learned latent spaces of VAEs are able to capture and represent differentiated Gaussian-like clusters of samples with similar genetic composition on a fine-scale from single nucleotide polymorphisms (SNPs), enabling applications in dimensionality reduction, data simulation, and imputation. These individual genotype sequences can then be decomposed into latent representations and reconstruction errors (residuals) which provide a sparse representation useful for lossless compression. We show that different population groups have differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, its effect on compression across populations, its relation to historical migrations, and we show how to introduce autoencoders into existing compression pipelines.

}, doi = {https://doi.org/10.1101/2023.09.27.558320}, url = {https://www.biorxiv.org/content/10.1101/2023.09.27.558320v1}, author = {Geleta, Margarita and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cBonet23, title = {HyperFast: Instant Classification for Tabular Data}, booktitle = {38th Annual AAAI Conference on Artificial Intelligence (AAAI)}, year = {2024}, month = {12/2024}, abstract = {

Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives require extensive hyperparameter tuning or work only in toy datasets under limited settings. In this paper, we introduce HyperFast, a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. HyperFast generates a task-specific neural network tailored to an unseen dataset that can be directly used for classification inference, removing the need for training a model. We report extensive experiments with OpenML and genomic data, comparing HyperFast to competing tabular data neural networks, traditional ML methods, AutoML systems, and boosting machines. HyperFast shows highly competitive results, while being significantly faster. Additionally, our approach demonstrates robust adaptability across a variety of classification tasks with little to no fine-tuning, positioning HyperFast as a strong solution for numerous applications and rapid model deployment. HyperFast introduces a promising paradigm for fast classification, with the potential to substantially decrease the computational burden of deep learning.

}, author = {Bonet, David and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cBarrabes, title = {Adversarial Learning for Feature Shift Detection and Correction}, booktitle = {Neural Information Processing Systems (NeurIPS)}, year = {2023}, month = {12/2023}, address = {New Orleans, USA}, abstract = {

TL;DR: We introduce a framework inspired in adversarial learning to detect and correct features originating a distribution shift between datasets.

Data shift is a phenomenon present in many real-world applications, and while there are multiple methods trying to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in multi-sensor data, where some sensors are malfunctioning, or in tabular and structured data, including biomedical, financial, and survey data, where faulty standardization and data processing pipelines can lead to erroneous features. In this work, we explore using the principles of adversarial learning, where the information from several discriminators trained to distinguish between two distributions is used to both detect the corrupted features and fix them in order to remove the distribution shift between datasets. We show that mainstream supervised classifiers, such as random forest or gradient boosting trees, combined with simple iterative heuristics, can localize and correct feature shifts outperforming current statistical and neural network-based techniques.

}, author = {Barrab{\'e}s, M{\'\i}riam and Mas-Montserrat, Daniel and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {aDominguez, title = {Neural ADMIXTURE: rapid population clustering with autoencoders}, journal = {Nature Computational Science}, year = {2023}, month = {07/2023}, abstract = {

Characterizing the genetic substructure of large cohorts has become increasingly important as genetic association and prediction studies are extended to massive, increasingly diverse, biobanks. ADMIXTURE and STRUCTURE are widely used unsupervised clustering algorithms for characterizing such ancestral genetic structure. These methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA marker frequencies. The assignments, and clusters, provide an interpretable representation for geneticists to describe population substructure at the sample level. However, with the rapidly increasing size of population biobanks and the growing numbers of variants genotyped (or sequenced) per sample, such traditional methods become computationally intractable. Furthermore, multiple runs with different hyperparameters are required to properly depict the population clustering using these traditional methods, increasing the computational burden. This can lead to days of compute. In this work we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as ADMIXTURE, providing similar (or better) clustering, while reducing the compute time by orders of magnitude. Indeed, the equivalent of one month of continuous compute can be reduced to hours. In addition, Neural ADMIXTURE can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. Our models can also be stored, allowing later cluster assignment to be performed with a linear computational time.

}, author = {Dominguez, Albert and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {xPerera22, title = {Ancestry-conditioned Generative Models for Genotyping}, year = {2022}, abstract = {

Local ancestry inference (LAI) identifies the ancestry of each segment of an individual{\textquoteright}s genome and it is a critical step in the analysis of human genomes with applications from pharmacogenomics and personalized medicine to increase detection of genetic associations.\ 

New LAI techniques are appearing at a fast pace in both industry and academic research and large data-sets of human genomic sequences from the ancestries of interest are required to train those methods. Usually, those data-sets are protected by privacy regulations, are proprietary or accessible only when they come with restrictions due to its nature. An interesting way to overcome those difficulties is through the generation of data samples that could be similar enough to real sequences from ancestries of interest. A generalized model can be openly shared because there is no real individual information in there.\ 

Thus, we present a class-conditional Generative adversarial Model and a Conditional Generative Moment-Matching Network intended to generate new realistic genotypes of a desired ancestry. In addition, we present a privacy mechanism that extracts features from the real data to generate new realistic genotypes by using features.

}, author = {Perera, Maria}, editor = {Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cMas-Montserrat, title = {Generative Moment Matching Networks for Genotype Simulation}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

The generation of synthetic genomic sequences using neural networks has potential to overcome privacy and data sharing restrictions and to mitigate potential bias within datasets due to under-representation of some population groups. However, there is not a consensus on which architectures, training procedures, and evaluation metrics should be used when simulating single nucleotide polymorphism (SNP) sequences with neural networks. In this paper, we explore the use of Generative Moment Matching Networks (GMMNs) for SNP simulation, we present some architectural and procedural changes to properly train the networks, and we introduce an evaluation scheme to qualitatively and quantitatively asses the quality of the simulated sequences.

}, author = {Mas-Montserrat, Daniel and Perera, Maria and Barrab{\'e}s, M{\'\i}riam and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {xBarrabes22, title = {Machine Learning for Genomic Sequence Processing}, year = {2022}, abstract = {

Introduction to Research, BSc Data Science and Engineering, Autumn 2021:

Predicting Dog Phenotypes from Genotypes

In this paper, we analyze dog genotypes {\textendash} positions of DNA sequences that often vary between different dogs {\textendash}in order to predict the corresponding phenotypes {\textendash} unique characteristics that result from different genetic code. More specifically, given chromosome data from a dog, we aim to predict its breed category, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We also investigate the use of a neural network (both in linear and non-linear modes) for breed classification and compare its performance to traditional statistical methods. We show that linear methods generally outperform or match the performance of non-linear methods for breed classification. However, the reverse case is true for height and weight regression. We also evaluate the results of all of these methods based on the number of input features used in the analy sis and demonstrate that phenotypes can be predicted with as few as 0.5\% of the input features, and dog breeds can be classified with 50\% balanced accuracy with as few as 0.02\% of the full genomic sequences for our analysis.

MergeGenome. A Python-based Toolkit for Merging VCF files

A challenge of genomic studies is the lack of easy to access and properly formatted datasets. When having access to more than one dataset, it seems desirable to combine them. There is a lack of tools to duly merge genomic datasets without losing all non-matching features. To fill this gap, we present the MergeGenome toolkit, designed to integrate DNA sequences from two files in variant call format (VCF) while targeting data quality. MergeGenome is a robust pipeline of comprehensive steps to standardize nomenclature, remove ambiguities, correct flips, eliminate mismatches, select important features, and filter likely erroneous features (the latter with machine learning). MergeGenome is Python-based and relies on pre-existing software for manipulation and imputation of VCF data. We evaluate the result of merging two datasets with dog DNA sequences of dissimilar lengths and notice that genotype imputation with Beagle v5.1 usually fails for low-frequency alleles. Alternatively, we explore several multi-label machine learning classifiers. Although K-Nearest Neighbors achieves competitive results, none of the methods tried outperforms Beagle v5.1.

}, author = {Barrab{\'e}s, M{\'\i}riam}, editor = {Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cBartusiak, title = {Predicting Dog Phenotypes from Genotypes}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

In this paper, we analyze dog genotypes - positions of DNA sequences that often vary between different dogs - in order to predict the corresponding phenotypes - unique characteristics that result from different genetic code. More specifically, given chromosome data from a dog, we aim to predict its breed category, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We show that linear methods generally outperform or match non-linear methods for breed classification. However, the reverse case is true for height and weight regression. We also evaluate the performance of all of these methods based on the number of input features used in the analysis. We conduct experiments using different fractions of the full genomic sequences and demonstrate that phenotypes can be predicted with as few as 0.5\% of the input features available for our analysis, and dog breeds can be classified with 50\% balanced accuracy with as few as 0.02\% of the features.

}, doi = {https://doi.org/10.1101/2022.04.13.488108 }, author = {Bartusiak, Emily and Barrab{\'e}s, M{\'\i}riam and Aigerim Rymbe and J{\'u}lia Gimbernat and Cayetana L{\'o}pez and Lorenzo Barberis and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {aBernal22, title = {SALAI-Net: species-agnostic local ancestry inference network}, journal = {Bioinformatics}, volume = {38}, year = {2022}, month = {09/2022}, chapter = {ii27}, abstract = {

Motivation
Local ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.

Results
We present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models{\textquoteright} ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.

}, doi = {https://doi.org/10.1093/bioinformatics/btac464}, url = {https://academic.oup.com/bioinformatics/article/38/Supplement_2/ii27/6701999?login=false$\#$373158629}, author = {Bernal, Oriol and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cGeleta, title = {Deep variational autoencoders for population genetics: applications in classification, imputation, dimensionality reduction, and novel lossless data compression}, booktitle = {American Society of Human Genetics (ASHG)}, year = {2021}, month = {10/2021}, publisher = {ASHG}, organization = {ASHG}, address = {Virtual}, abstract = {

In this study we show the power of variational autoencoders (VAEs) for a variety of tasks relating to the interpretation and compression of genomic data. The unsupervised setting allows for detecting and learning of granular population structure and inferring of new informative latent factors, opening up an avenue for applications in dimensionality reduction, data simulation, population classification, imputation, and lossless genomic data compression. The latent spaces of VAEs are able to capture and represent clearly differentiated Gaussian-like clusters of similar genetic composition on a fine-scale with a relatively small number of SNPs as input. Furthermore, sequences can be decomposed into latent representations and reconstruction errors (residuals) providing a sparse representation that provides a means for efficient lossless compression.

Identifying genetic clusters can be important when performing genome-wide association studies and provides an alternative to self-reported ethnic labels, which are culturally constructed and vary according to the location and individual. A variety of unsupervised dimensionality reduction methods have been explored in the past for such applications, including PCA, MDS, t-SNE, and UMAP. Our proposed VAE can represent the population structure as a Gaussian-distributed continuous multi-dimensional representation and as classification probabilities providing flexible and interpretable population descriptors.

We train our VAE method with several worldwide whole genome datasets from both humans and canids and evaluate the performance of the different proposed applications with networks with and without ancestry conditioning. Our experiments show that different population groups have significantly differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, noting its effect on compression across populations and connect these patterns to historical migrations and ancestral relationships.

Video from the related BSc thesis at UPC Data Science Engineering (2021):

}, author = {Geleta, Margarita and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cDominguez, title = {Neural ADMIXTURE: rapid population clustering with autoencoders}, booktitle = {2021 Society for Molecular Biology \& Evolution meeting (SMBEv2021)}, year = {2021}, month = {06/2021}, address = {Virtual}, abstract = {

Characterizing the genetic substructure of large cohorts has become increasingly important as genetic association and prediction studies are extended to massive, increasingly diverse, biobanks. ADMIXTURE and STRUCTURE are widely used unsupervised clustering algorithms for characterizing such ancestral genetic structure. These methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA marker frequencies. The assignments, and clusters, provide an interpretable representation for geneticists to describe population substructure at the sample level. However, with the rapidly increasing size of population biobanks and the growing numbers of variants genotyped (or sequenced) per sample, such traditional methods become computationally intractable. Furthermore, multiple runs with different hyperparameters are required to properly depict the population clustering using these traditional methods, increasing the computational burden. This can lead to days of compute. In this work we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as ADMIXTURE, providing similar (or better) clustering, while reducing the compute time by orders of magnitude. In addition, this network can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. These models can also be stored, allowing later cluster assignment to be performed with a linear computational time.

}, author = {Dominguez, Albert and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cMayoral21b, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Silvia Ingala and Luigi Lorenzini and Alle Meije Wink and Sven Haller and Jose Luis Molinuevo and Robin Wolz and Alessandro Palombit and Adam J Schwarz and Ga{\"e}l Chetelat and Pierre Payoux and Pablo Martinez-Lage and Giovanni Frisoni and Nick C Fox and Craig W Ritchie and Joanna M Wardlaw and Adam Waldman and Frederik Barkhof and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @mastersthesis {xOriola, title = {Species-agnostic Local Ancestry Inference on Genomic Data with Convolutions}, year = {2021}, abstract = {

Local Ancestry Inference (LAI) is the high resolution prediction of ancestry (African, European, ...) across a DNA sequence. LAI is becoming increasingly important in DNA sequence analysis for the study of human ancestry and migrations. It is also necessary for polygenic risk scores research (prediction of traits and disease risk). Most current LAI models are built for specific species, set of ancestries and chromosomes, hence a new model needs to be trained from scratch for every slightly different setting. This creates a big barrier for research and industry to shift across different LAI scenarios. In this thesis we present SALAI-Net, the first statistical method for LAI with reference panel that can be used on any set of species and ancestries (species-agnostic). Loter is the state of the art in species-agnostic models with reference panel, and is based on a dynamic programming algorithm. However, it is slow and does not perform very well in small reference panel settings. Our model is based on a novel hand-engineered template matching block followed by a convolutional smoothing filter optimized to minimize cross-entropy loss on a training dataset. The right choice of DNA sequence encoding, similarity features and architecture is what makes our model able to generalize well to unseen ancestries, species, and different chromosomes. We benchmark our models on whole genome data of humans and we test the ability to generalize to dog species when trained on human data. Our models outperform the state-of-the-art method by a big margin in terms of accuracy, testing in different settings and datasets. Moreover, it is up to two orders of magnitude faster. Our model also shows close to no generalization gap when switching between species.

}, author = {Oriol, Benet}, editor = {Mas-Montserrat, Daniel and Ioannidis, Alexander G. and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xIturralde, title = {Towards video alignment across cameras with sign language 2D poses}, year = {2021}, abstract = {

This thesis degree is part of a project from the Image Group at UPC that is focused on sign language translation using deep learning technologies. This thesis builds on top of an existing database called How2Sign, that contains more than 83 hours of sign language translation videos. This database has some textual annotations aligned to a front RGB camera. The same scenes are also captured by a side RGB and a front RGB-D cameras. These three cameras are not synchronized, so it is necessary to align the segments annotated on the RGB front camera to the other cameras. This thesis explores a solution based on the cross correlation operator. Our work is to process the coordinates of the joints of the subject that appears in the videos, not from the point of view of image or video processing based on pixels. The first part if this thesis is to investigate the properties of the cross-correlation function by locating short video segments of a long recording based on automatically extracted 2D human poses. The experiments studied the impact of adding noise. The second part applied the cross-correlation to try to align two videos with the same scene, but recorded with different cameras from different points of view.

}, author = {Andrea Iturralde}, editor = {Laia Tarr{\'e}s and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {dGeleta21, title = {Unsupervised learning with applications in genomic}, volume = {BSc Data Science Engineering}, year = {2021}, abstract = {

In this study we show the power of variational autoencoders (VAEs) for a variety of tasks relating to the interpretation and compression of genomic data. The unsupervised setting allows for detecting and learning of granular population structure and inferring of new informative latent factors, opening up an avenue for applications in dimensionality reduction, data simulation, population classification, imputation, and lossless genomic data compression. The latent spaces of VAEs are able to capture and represent clearly differentiated Gaussian-like clusters of similar genetic composition on a fine-scale with a relatively small number of Single Nucleotide Polymorphisms (SNPs) as input. Furthermore, sequences can be decomposed into latent representations and reconstruction errors (residuals) providing a sparse representation that provides a means for efficient lossless compression.

Identifying genetic clusters can be important when performing genome-wide association studies and provides an alternative to self-reported ethnic labels, which are culturally constructed and vary according to the location and individual. A variety of unsupervised dimensionality reduction methods have been explored in the past for such applications, including PCA, MDS, t-SNE, and UMAP. Our proposed VAE can represent the population structure as a Gaussian-distributed continuous multi-dimensional representation and as classification probabilities providing flexible and interpretable population descriptors.

We train our VAE method with several worldwide whole genome datasets from both humans and canids, and evaluate the performance of the different proposed applications with networks with and without ancestry conditioning. Our experiments show that different population groups have significantly differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, noting its effect on compression across populations and connect these patterns to historical migrations and ancestral relationships.

}, author = {Geleta, Margarita}, editor = {Mas-Montserrat, Daniel and Ioannidis, Alexander G. and Xavier Gir{\'o}-i-Nieto} } @conference {cIsart, title = {CNN-based bacilli detection in sputum samples for tuberculosis diagnosis}, booktitle = {International Symposium on Biomedical Imaging (ISBI 2019)}, year = {2019}, month = {04/2019}, abstract = {

Tuberculosis (TB) is one of the infectious diseases that causes more deaths in low and middle-income countries. A low-cost method to diagnose TB consists in analyzing sputum smear samples through microscope observation. Manual identification and counting of bacilli is a very time consuming task and the sensitivity of the diagnosis depends on the availability of skilled technicians. We propose a computer vision technique based on a convolutional neural network (CNN) to automatically segment and count bacilli in sputum samples and predict the infection level.

}, author = {Antoni Isart and Mateu Espasa and Ver{\'o}nica Vilaplana and Elisa Sayrol} } @conference {cIndia18, title = {UPC Multimodal Speaker Diarization System for the 2018 Albayzin Challenge}, booktitle = {IberSpeech 2018}, year = {2018}, month = {11/2018}, address = {Barcelona}, abstract = {

This paper presents the UPC system proposed for the Multimodal Speaker Diarization task of the 2018 Albayzin Challenge. This approach works by processing individually the speech and the image signal. In the speech domain, speaker diarization is performed using identity embeddings created by a triplet loss DNN that uses i-vectors as input. The triplet DNN is trained with an additional regularization loss that minimizes the variance of both positive and negative distances. A sliding window is then used to compare speech segments with enrollment speaker targets using cosine distance between the embeddings. To detect identities from the face modality, a face detector followed by a face tracker has been used on the videos. For each cropped face a feature vector is obtained using a Deep Neural Network based on the ResNet 34 architecture, trained using a\  metric learning triplet loss (available from dlib library). For each track the face feature vector is obtained by averaging the features obtained for each one of the frames of that track. Then, this feature vector is compared with the features extracted from the images of the enrollment identities. The proposed system is evaluated on the RTVE2018 database.

}, keywords = {Face Diarization, Multimodal Systems, Speaker Diarization}, author = {India, M. and Sagastiberri, I. and Palau, P. and Elisa Sayrol and Morros, J.R. and Hernando, J.} } @conference {cLe, title = {Towards large scale multimedia indexing: A case study on person discovery in broadcast news}, booktitle = {International Workshop on Content-Based Multimedia Indexing - CBMI 2017}, year = {2017}, month = {06/2017}, address = {Firenze, Italy}, abstract = {

The rapid growth of multimedia databases and the human interest in their peers make indices representing the location and identity of people in audio-visual documents essential for searching archives. Person discovery in the absence of prior identity knowledge requires accurate association of audio-visual cues and detected names. To this end, we present 3 different strategies to approach this problem: clustering-based naming, verification-based naming, and graph-based naming. Each of these strategies utilizes different recent advances in unsupervised face / speech representation, verification, and optimization. To have a better understanding of the approaches, this paper also provides a quantitative and qualitative comparative study of these approaches using the associated corpus of the Person Discovery challenge at MediaEval 2016. From the results of our experiments, we can observe the pros and cons of each approach, thus paving the way for future promising research directions.

}, author = {Nam Le and Herv{\'e} Bredin and Gabriel Sargent and Miquel India and Paula Lopez-Otero and Claude Barras and Camille Guinaudeau and Guillaume Gravier and Gabriel Barbosa da Fonseca and Izabela Lyon Freire and Zenilton Patroc{\'\i}nio Jr. and Silvio Jamil F. Guimaraes and Gerard Mart{\'\i} and Morros, J.R. and Javier Hernando and Laura Docio-Fernandez and Carmen Garcia-Mateo and Sylvain Meignier and Jean-Marc Odobez} } @conference {cIndia, title = {UPC System for the 2016 MediaEval Multimodal Person Discovery in Broadcast TV task}, booktitle = {MediaEval 2016 Workshop}, year = {2016}, month = {10/2016}, address = {Hilversum, The Netherlands}, abstract = {

The UPC system works by extracting monomodal signal segments (face tracks, speech segments) that overlap with the person names overlaid in the video signal. These segments are assigned directly with the name of the person and used as a reference to \ compare against the non-overlapping (unassigned) signal segments. This process is performed independently both on the speech and video signals. A simple fusion scheme is used to combine both monomodal annotations into a single one.

}, author = {India, M. and Mart{\'\i}, G. and Cotillas, C. and Bouritsas, G. and Elisa Sayrol and Morros, J.R. and Hernando, J.} } @conference {cIndia15, title = {UPC System for the 2015 MediaEval Multimodal Person Discovery in Broadcast TV task}, booktitle = {MediaEval 2015 Workshop}, year = {2015}, month = {09/2015}, address = {Wurzen, Germany}, abstract = {

This paper describes a system to identify people in broadcast TV shows in a purely unsupervised manner. The system outputs the identity of people that appear, talk and can be identified by using information appearing in the show (in our case, text with person names). Three types of monomodal technologies are used: speech diarization, video diarization and text detection / named entity recognition. These technologies are combined using a linear programming approach where some restrictions are imposed.

}, author = {India, M. and David Varas and Ver{\'o}nica Vilaplana and Morros, J.R. and Hernando, J.} } @mastersthesis {xPereira, title = {An investigation of eye gaze tracking utilities in image object recognition}, year = {2014}, abstract = {

Computer vision has been one of the most revolutionary technologies of the last few decades. This project investigates how to improve an image recognition system (image classifier) using a not very exploded technology; eye gaze tracking. The aim of this project is to explore the benefits that this technology can bring to an image classifier. The experiment that is set in this project is to build a dataset with an eye tracking device and (using different sized cropped parts of the image based on the eye tracking data) see how the performance of an image classifier is affected with these images. The results are interesting. Since smaller images have to be processed by using this method, the system is more efficient. Regarding the performance, it is very similar to the one obtained without using any eye tracking data, so it is arguable to state that it presents an improvement, and opens new directions of investigation for future works.

Poster


}, author = {Imedio-Pereira, Sergi}, editor = {Mohedano, Eva and Xavier Gir{\'o}-i-Nieto and Albatal, Rami and O{\textquoteright}Connor, N.} } @conference {cIrurueta12, title = {PROMEDS: An adaptive robust fundamental matrix estimation approach}, booktitle = {3DTV Conference}, year = {2012}, month = {09/2012}, publisher = {IEEE}, organization = {IEEE}, address = {Zurich, Switzerland}, abstract = {

Accurate fundamental matrix estimation from computed correspondences is hard to achieve depending on the constraints on computational time and available data (i.e. correspondences and quality scores). Several algorithms exist for this task, like the 8-points, the 7-points algorithm or robust methods such as RANSAC, MSAC or LMedS. Robust methods are capable of discriminating correspondence outliers, thus, obtaining better results. Additionally, some variations of the previous methods have been proposed. For instance PROSAC is an improvement of RANSAC which takes into account additional information of the quality of the matches to largely reduce the computational cost of the fundamental matrix estimation process. This work proposes a new robust method for fundamental matrix estimation that combines the benefits of PROSAC and LMedS algorithms, namely improved quality, reduced computational time and less parameters to adjust.

}, keywords = {Fundamental Matrix, LMedS, PROSAC}, author = {Irurueta, A. and Morros, J.R.} } @phdthesis {dIregui08, title = {Efficient strategies for navigation through very large JPEG2000 image}, year = {2008}, school = {Universit{\'e} Catholique de Louvain (UCL)}, type = {phd}, url = {http://hdl.handle.net/2078.1/19637 }, author = {Iregui, M.}, editor = {Marqu{\'e}s, F. and Macq, B.} } @article {aDescampe07, title = {Prefetching and caching strategies for remote and interactive browsing of JPEG2000 images}, journal = {IEEE transactions on image processing}, volume = {16}, number = {5}, year = {2007}, pages = {1339{\textendash}1354}, issn = {1057-7149}, author = {Descampe, A. and Vleeschouwer, D. and Iregui, M. and Macq, B. and Marqu{\'e}s, F.} } @conference {cDescampe06, title = {Pre-fetching strategies for remote and interactive browsing of JPEG2000 images}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {3203{\textendash}3206}, author = {Descampe, A. and De Vleeschouwer, C. and Iregui, M. and Macq, B. and Marqu{\'e}s, F.} } @conference {cO{\textquoteright}Connor03, title = {Region and object segmentation algorithms in the QIMERA segmentation platform}, booktitle = {Third International Workshop on Content-Based Multimedia Indexing}, year = {2003}, pages = {95{\textendash}103}, isbn = {978-84-612-2373-2}, author = {O{\textquoteright}Connor, N. and Sav, S. and Adamek, T. and Mezaris, V. and Kompatsiaris, I. and Lui, T. and Izquierdo, E. and Ferran, C. and Casas, J.} }