To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the items relevant to the query; An approximate distance computation mechanism to rapidly scan these lists. While supervised deep learning has recently enabled improvements to the latter, t…
This article presents an empirical study that investigated and compared two “big data” text analysis methods: dictionary-based analysis, perhaps the most popular automated analysis approach in social science research, and unsupervised topic modeling (i.e., Latent Dirichlet Allocation [LDA] analysis), one of the most widely used algorithms in the field of compute…
The ability of multimedia data to attract and keep people’s interest for longer periods of time is gaining more and more importance in the fields of information retrieval and recommendation, especially in the context of the ever growing market value of social media and advertising. In this chapter we introduce a benchmarking framework (dataset and evaluation too…
Interestingness is the quantification of the ability of an imageto induce interest in a user. Because defining and interpretinginterestingness remain unclear in the literature, we introduce inthis paper two new notions, intra- and inter-interestingness, andinvestigate a novel set of dedicated experiments.More specifically, we propose four experimental protocols:…
Inductive matrix completion (IMC) is a model for incorporating side information in form of “features” of the row and column entities of an unknown matrix in the matrix completion problem. As side information, features can substantially reduce the number of observed entries required for reconstructing an unknown matrix from its given entries. The IMC problem can …
This paper deals with the unification of local and non-local signal processing on graphs within a single convolutional neural network (CNN) framework. Building upon recent works on graph CNNs, we propose to use convolutional layers that take as inputs two variables, a signal and a graph, allowing the network to adapt to changes in the graph structure. In this ar…
This paper presents the work done at Technicolor regardingthe MediaEval 2016 Predicting Media Interestingness Task,which aims at predicting the interestingness of individual im-ages and video segments extracted from Hollywood movies.We participated in both the image and video subtasks.
Easy accessibility can often lead to over-consumption, as seen in food and alcohol habits. On video on-demand (VOD) services, this has recently been referred to as binge watching, where potentially entire seasons of TV shows are consumed in a single viewing session. While a user viewership model may reveal this binging behavior, creating an accurate model has se…
We present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through user actions or behaviors. Our task is to inf…
This paper summarizes the computational models that Technicolor proposes to predict interestingness of images and videos within the MediaEval 2017 PredictingMedia Interestingness Task. Our systems are based on deep learning architectures and exploit the use of both semantic and multimodal features. Based on the obtained results, we discuss our findings and obtai…
An infrastructure-less indoor localization system is proposed based on fingerprints of light signals acquired at high frequencies. In contrast to other systems that modulate lights, the proposed system distinguishes lights by learning from training samples. Due to slight differences in the electronic components used in the construction of compact fluorescent lig…
This paper addresses the example-based stylization of videos. Style transfer aims at editing an image so that it matches the style of an example. This topic has recently been investigated massively, both in the industry and academia. The difficulty lies in how to capture the style of an image. For this work we build on our previous work " Split and Match " for s…
As a new generation of smartwatches enters the market, one common use is for displaying information such as notifications. While some content might warrant immediately interrupting a user, there is also information that might be important to display yet less urgent. It would be useful to show this content on the watch but not immediately draw the user's attentio…
Accounts are often shared by multiple users, each of them having different item consumption and temporal habits. Identifying of the active user can lead to improvements in a variety of services by switching from account personalized services to user personalized services. To do so, we develop a topic model extending the Latent Dirichlet Allocation using a hidden…
Electroencephalography (EEG)-based emotion recognition is currently a hot issue in the affective computing community. Numerous studies have been published on this topic, following generally the same schema: 1) presentation of emotional stimuli to a number of subjects during the recording of their EEG, 2) application of machine learning techniques to classify the…
This paper tackles the task of storing a large collection of vectors, such as visual descriptors, and of searching in it. To this end, we propose to approximate database vectors by constrained sparse coding, where possible atom weights are restricted to belong to a finite subset. This formulation encompasses, as particular cases, previous state-of-the-art method…
Les liens DSL peuvent subir des pannes sporadiques entraînant des deconnexions ou un acces Internet dégradé. Ces pannes sont a l’origine d’une expérience utilisateur négative et générent des coûts pour les fournisseurs d’accès Internet (FAI) via des appels d’assistance technique. La prediction de pannes permet aux FAI de mettre en oeuvre des mesures proactives d…
Neural node embedding has been recently developed as a powerful representation for supervised tasks with graph data. We leverage this recent advance and propose a novel approach for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate consistent improvement of the proposed approach ov…
In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network with an expert-designed generative model that serves as decoder. The core innovation is our new differentiable par…
The success of Google’s PageRank algorithm popularized graphs as a tool to model the web’s navigability. At that time, the web topology was resulting from human edition of hyper-links. Nowadays, that topology is mostly resulting from algorithms. In this paper, we propose to study the topology realized by a class of such algorithms: recommenders. By modeling the …
Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. A common approach is to rely on Product Quantization, which allows the storage of large vector databases in memory and efficient distance computations. Yet, implementations of nearest neighbor search with Product Quantization have their perform…
"A recently celebrated kind of deep neural networks is Generative Adversarial Networks. GANs are generators of samples from a distribution that has been learned; they are up to now centrally trained from local data on a single location.
We consider example-guided audio source separation approaches, where the audio mixture to be separated is supplied with source examples that are assumed matching the sources in the mixture both in frequency and time. These approaches were successfully applied to the tasks such as source separation by humming, score-informed music source separation, and music sou…
In this paper, the Predicting Media Interestingness task which is running for the second year as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation, is presented. For the task, participants are expected to create systems that automatically select images and video segments that are considered to be the most interesting for a common viewe…
Adaptive transform learning schemes have been extensively studied in the literature with a goal to achieve better compression efficiency compared to extensively used Discrete Cosine Transforms (DCT) inside a video codec. These transforms are learned offline on a large training set and are tested either in competition with or in place of the core transforms i.e. …
Zepeda and Perez [41] have recently demonstrated the promise of the exemplar SVM (ESVM) as a feature encoder for image retrieval. This paper extends this approach in several directions: We first show that replacing the hinge loss by the square loss in the ESVM cost function significantly reduces encoding time with negligible effect on accuracy. We call this mode…
Learning parameters from voluminous data can be prohibitive in terms of memory and computational requirements. We propose a ‘compressive learning’ framework, where we estimate model parameters from a sketch of the training data. This sketch is a collection of generalized moments of the underlying probability distribution of the data. It can be computed in a sing…
This paper provides an overview of the Predicting MediaInterestingness task that is organized as part of the Media-Eval 2016 Benchmarking Initiative for Multimedia Evalua-tion. The task, which is running for the first year, expectsparticipants to create systems that automatically select images and video segments that are considered to be the mostinteresting for …
In this paper we tackle the problem of single channel audio source separation driven by descriptors of the sounding object's motion. As opposed to previous approaches, motion is included as a soft-coupling constraint within the nonnegative matrix factorization framework. The proposed method is applied to a multimodal dataset of instruments in string quartet perf…
A large portion of data mining and analytic services use modern machine learning techniques, such as deep learning. The state-of-the-art results by deep learning come at the price of an intensive use of computing resources. The leading frameworks (e.g., TensorFlow) are executed on GPUs or on high-end servers in datacenters. On the other end, there is a prolifera…
Détecter au plus tôt les utilisateurs importants dans les réseaux sociaux est un problème majeur. Les services de classement d'utilisateurs (peer ranking) sont maintenant des outils bien établis, par des sociétés comme PeerIndex, Klout ou Kred. Leur fonction est de ``classer'' les utilisateurs selon leur influence. Cette notion est néanmoins abstraite, et les mé…
Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. Because it offers low responses times, Product Quantization (PQ) is a popular solution. PQ compresses high-dimensional vectors into short codes using several sub-quantizers, which enables in-RAM storage of large databases. This allows fast answ…
Single Index Models (SIMs) are simple yet flexible semi-parametric models for machine learning, where the response variable is modeled as a monotonic function of a linear combination of features. Estimation in this context requires learning both the feature weights and the nonlinear function that relates features to observations. While methods have been describe…
We propose a novel informed source separation method for audio object coding based on a recent sampling theory for smooth signals on graphs. Assuming that only one source is active at each time-frequency point, we compute an ideal map indicating which source is active at each time-frequency point at the encoder. This map is then sampled with a compressive graph …
Several high-dimensional learning applications require the parameters to satisfy a “group sparsity” constraint, where clusters of coefficients are required to be simultaneously selected or rejected. The group lasso and its variants are common methods to solve problems of this form. Recently, in the standard sparse setting, it has been noted that tighter convex r…
Predicting interestingness of media content remains an important, but challenging research subject. The difficulty comes first from the fact that, besides being a high-level semantic concept, interestingness is highly subjective and its global definition has not been agreed yet. This paper presents the use of up-to-date deep learning techniques for solving the t…
For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants are usually employed to achieve such compression while minimizing the loss of accuracy. Yet, unlike binary hashing schemes, these unsupervised methods have not yet benefited from the …
In this paper we propose a new method to automatically select the rank of linear transforms during supervised learning. Our approach relies on a sparsity-enforcing element-wise soft-thresholding operation applied after the linear transform. This novel approach to supervised rank learning has the important advantage that it is very simple to implement and incurs …
This work concerns sampling of smooth signals on arbitrary graphs. We first study a structured sampling strategy for such smooth graph signals that consists of a random selection of few pre-defined groups of nodes. The number of groups to sample to stably embed the set of $k$-bandlimited signals is driven by a quantity called the \emph{group} graph cumulative co…
La recommandation joue un rôle central dans le e-commerce et dans l'industrie du divertissement. L'intérêt croissant pour la transparence algorithmique nous motive dans cet article à observer les résultats de recommandations sous la forme d'un graphe capturant les navigations proposées dans l'espace des items. Nous argumentons qu'une telle approche en "boite noi…
Estimating the inverse covariance matrix of p variables from n observations is challenging when n p, since the sample covariance matrix is singular and cannot be inverted. A popular solution is to optimize for the `1 penalized estimator; however, this does not incorporate structure domain knowledge and can be expensive to optimize. We consider finding inverse …
Ensuring the early detection of important social network users is a challenging task. Some peer ranking services are now well established, such as PeerIndex, Klout, or Kred. Their function is to rank users according to their influence. This notion of influence is however abstract, and the algorithms achieving this ranking are opaque. Following the rising demand …
This paper presents an open database of annotated film clips together with an analysis of elements of film style related to how the shots are composed, how the transitions are performed between shots and how the shots are sequenced to compose a film unit. The purpose is to initiate a shared repository pertaining to elements of film style which can be used by com…
We consider inverse covariance estimation with group sparsity. The groups areoverlapping principal submatrices, which may correspond to structural similarity(e.g. pixels in adjacent regions) or categories (e.g. voter party loyalties). Wepropose a scalable method that makes use of chordal decomposition and appliesthe Frank-Wolfe algorithm. For small simulated pro…
Matrix completion (MC) with additional information has found wide applicability in several machine learning applications. Among algorithms for solving such problems, Inductive Matrix Completion(IMC) has drawn a considerable amount of attention, not only for its well established theoretical guarantees but also for its superior performance in various real-world ap…
In this work, we propose a framework, dubbed Union-of-Subspaces SVM (US-SVM), to learn linear classifiers as sparse codes over a learned dictionary. In contrast to discriminative sparse coding with a learned dictionary, it is not the data but the classifiers that are sparsely encoded. Experiments in visual categorization demonstrate that, at training time, the j…
The aggregation of image statistics – the so-called pooling step of image classification algorithms – as well as the construction of part-based models, are two distinct and well-studied topics in the literature. The former aims at leveraging a whole set of local descriptors that an image can contain (through spatial pyramids or Fisher vectors for instance) while…
“To be considered for the 2017 IEEE Jack Keil Wolf ISIT Student Paper Award.” In this paper we study the problem of noisy tensor completion for tensors that admit a canonical polyadic or CANDE-COMP/PARAFAC (CP) decomposition with one of the factors being sparse. We present general theoretical error bounds for an estimate obtained by using a complexity-regularize…
Time series prediction problems are becoming increasingly high-dimensional in modern applications, such as climatology and demand forecasting. For example, in the latter problem, the number of items for which demand needs to be forecast might be as large as 50,000. In addition, the data is generally noisy and full of missing values. Thus, modern applications req…
Style transfer' among images has recently emerged as a very active research topic, fuelled by the power of convolution neural networks (CNNs), and has become fast a very popular technology in social media. This paper investigates the analogous problem in the audio domain: How to transfer the style of a reference audio signal to a target audio content? We propose…
Some structural characteristics of online discussions have been successfully modeled in the recent years. When parameters of these models are properly estimated, the models are able to generate synthetic discussions that are structurally similar to the real discussions. A common aspect of these models is that they consider that all users behave according to the …
Image retrieval in large image databases is an important problem that drives a number of applications. Yet the use of supervised approaches that address this problem has been limited due to the lack of large labeled datasets for training. Hence, in this paper we introduce two new datasets composed of images extracted from publicly available videos from the Cable…
We consider the problem of identifying people on the basis of their walk (gait) pattern. Classical approaches to tackle this problem are based on, e.g., video recordings or piezoelectric sensors embedded in the floor. In this work, we rely on acoustic and vibration measurements, obtained from a microphone and a geophone sensor, respectively. The contribution of …
Memorability of media content such as images and videos has recently become an important research subject in computer vision. This paper presents our computation model for predicting image memorability, which is based on a deep learning architecture designed for a classification task. We exploit the use of both convolutional neural network (CNN) - based visual f…
Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overlapping) groups. For very large datasets and under standard sparsity constraints, hard thresholding methods have proven to be extremely efficient, but such methods require NP hard projections when dealing with overlappin…
Wi-Fi is the preferred way of accessing the Internet for many devices at home, but it is vulnerable to performance problems. The analysis of Wi-Fi quality metrics such as RSSI or PHY rate may indicate a number of problems, but users may not notice many of these problems if they don't degrade the performance of the applications they are using. In this work, we st…
Recent work in video compression has shown that using multiple 2D transforms instead of a single transform in order to de-correlate residuals provides better compression efficiency. These transforms are tested competitively inside a video encoder and the optimal transform is selected based on the Rate Distortion Optimization (RDO) cost. However, one needs to enc…