Co-Regularized Deep Representations for Video Summarization

Compact keyframe-based video summaries are a popular way of generating viewership on video sharing platforms. Yet, creating relevant and compelling summaries for arbitrarily long videos with a small number of keyframes is a challenging task. We propose a comprehensive keyframe-based summarization framework combining deep convolutional neural networks and restricted Boltzmann machines. An original co-regularization scheme is used to discover meaningful subject-scene associations. The resulting multimodal representations are then used to select highly-relevant keyframes. A comprehensive user study is conducted comparing our proposed method to a variety of schemes, including the summarization currently in use by one of the most popular video sharing websites. The results show that our method consistently outperforms the baseline schemes for any given amount of keyframes both in terms of attractiveness and informativeness. The lead is even more significant for smaller summaries.Comment: Video summarization, deep convolutional neural networks, co-regularized restricted Boltzmann machine

Group Invariant Deep Representations for Image Instance Retrieval

Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, representations extracted from Convolutional Neural Networks (CNN) are quickly gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors for image instance retrieval. While CNN-based descriptors are generally remarked for good retrieval performance at lower bitrates, they nevertheless present a number of drawbacks including the lack of robustness to common object transformations such as rotations compared with their interest point based FV counterparts. In this paper, we propose a method for computing invariant global descriptors from CNNs. Our method implements a recently proposed mathematical theory for invariance in a sensory cortex modeled as a feedforward neural network. The resulting global descriptors can be made invariant to multiple arbitrary transformation groups while retaining good discriminativeness. Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated. We also show that our method which has few parameters is not prone to overfitting: improvements generalize well across datasets with different properties with regard to invariances. Finally, we show that our descriptors are able to compare favourably to other state-of-the-art compact descriptors in similar bitranges, exceeding the highest retrieval results reported in the literature on some datasets. A dedicated dimensionality reduction step --quantization or hashing-- may be able to further improve the competitiveness of the descriptors

En temps de crise, prendre le temps

La saison estivale constitue un moment propice, pour de nombreux chercheurs, pour se rencontrer à l’occasion de conférences, débattre des enjeux scientifiques et de l’actualité politique qui traversent notamment le monde de la recherche. Elle permet aussi à chacun de faire le point et d’avancer sur ses différents projets. Mais cette année plus encore que les précédentes, la période estivale s’avère singulière. La succession des crises qui ont ébranlé le monde de la recherche ces derniers mois..

Investigating the Aroma of Syrah Wines from the Northern Rhone Valley Using Supercritical CO2-Dearomatized Wine as a Matrix for Reconstitution Studies

This study aimed to investigate the key compounds involved in the aroma of French Syrah wines from the northern Rhone valley from two vintages characterized by distinct climatic conditions. The volatile composition of the wines was assessed through the determination of 76 molecules. After identifying the best matrix and best model for aroma reconstitution studies, omission tests were conducted using the Pivot profile method. For both vintages, 35 molecules with odor activity values (OAVs) above 0.5 were identified. While remarkably high levels of 2-furfurylthiol (FFT) were reported in both wines, rotundone and 3-sulfanylhexanol (3SH) enabled the strongest discrimination between the two wines. Wine dearomatized using supercritical carbon dioxide (sCO2) was identified as the best matrix. The best models built using this matrix were composed of molecules with OAV > 5 and OAV > 10 highlighting that this dearomatization approach can be valuable to reconstitute the aroma of wine using a small number of molecules. For the cool vintage wine, the omission of rotundone and FFT had the greatest impact on the olfactive profile for nonanosmic and anosmic respondents to rotundone, respectively. 3SH, whose omission decreased the rating of the “fruity” attribute, was identified as the main contributor to the aroma of Syrah wine produced in the warm vintage

Cancer screening in France: subjects’ and physicians’ attitudes

International audienceOBJECTIVE: Since screening for cancer has been advocated, funded, and promoted in France, it is important to evaluate the attitudes of subjects in the general population and general practitioners (GPs) toward cancer screening strategies. METHODS: EDIFICE is a nationwide opinion poll that was carried out by telephone among a representative sample of 1,504 subjects living in France and aged between 40 and 75 years and among a representative sample of 600 GPs. The questionnaire administered to subjects queried about previous screening for cancer. RESULTS: Ninety-three percent of women stated that they had undergone at least one mammography. Although rated "A" recommendation-strongly recommended-by the US Preventive Services Task Force, screening for colorectal cancer received less attention than prostate cancer screening which is rated "I"-insufficient evidence-(reported screening rates of 25% and 36%, respectively). Six percent of subjects stated that they had undergone lung cancer screening. GPs' attitudes toward cancer screening showed similar inconsistencies. CONCLUSIONS: It thus appears that understanding of cancer screening practices in the French general population does not match scientific evidence. To a lesser extent, this also holds for GPs

Impact of organised programs on colorectal cancer screening

<p>Abstract</p> <p>Purpose</p> <p>Colorectal cancer (CRC) screening has been shown to decrease CRC mortality. Organised mass screening programs are being implemented in France. Its perception in the general population and by general practitioners is not well known.</p> <p>Methods</p> <p>Two nationwide observational telephone surveys were conducted in early 2005. First among a representative sample of subjects living in France and aged between 50 and 74 years that covered both geographical departments with and without implemented screening services. Second among General Practionners (Gps). Descriptive and multiple logistic regression was carried out.</p> <p>Results</p> <p>Twenty-five percent of the persons(N = 1509) reported having undergone at least one CRC screening, 18% of the 600 interviewed GPs reported recommending a screening test for CRC systematically to their patients aged 50–74 years. The odds ratio (OR) of having undergone a screening test using FOBT was 3.91 (95% CI: 2.49–6.16) for those living in organised departments (referent group living in departments without organised screening), almost twice as high as impact educational level (OR = 2.03; 95% CI: 1.19–3.47).</p> <p>Conclusion</p> <p>CRC screening is improved in geographical departments where it is organised by health authorities. In France, an organised screening programs decrease inequalities for CRC screening.</p

Représentations compactes et invariantes à l'aide de l'apprentissage profond pour la recherche d'images par similarité

Nous avons précédemment mené une étude comparative entre les descripteurs FV et CNN dans le cadre de la recherche par similarité d’instance. Cette étude montre notamment que les descripteurs issus de CNN manquent d’invariance aux transformations comme les rotations ou changements d’échelle. Nous montrons dans un premier temps comment des réductions de dimension (“pooling”) appliquées sur la base de données d’images permettent de réduire fortement l’impact de ces problèmes. Certaines variantes préservent la dimensionnalité des descripteurs associés à une image, alors que d’autres l’augmentent, au prix du temps d’exécution des requêtes. Dans un second temps, nous proposons la réduction de dimension emboitée pour l’invariance (NIP), une méthode originale pour la production, à partir de descripteurs issus de CNN, de descripteurs globaux invariants à de multiples transformations. La méthode NIP est inspirée de la théorie pour l’invariance “i-theory”, une théorie mathématique proposée il y a peu pour le calcul de transformations invariantes à des groupes au sein de réseaux de neurones acycliques. Nous montrons que NIP permet d’obtenir des descripteurs globaux compacts (mais non binaires) et robustes aux rotations et aux changements d’échelle, que NIP est plus performants que les autres méthodes à dimensionnalité équivalente sur la plupart des bases de données d’images. Enfin, nous montrons que la combinaison de NIP avec la méthode de hachage RBMH proposée précédemment permet de produire des codes binaires à la fois compacts et invariants à plusieurs types de transformations. La méthode NIP+RBMH, évaluée sur des bases de données d’images de moyennes et grandes échelles, se révèle plus performante que l’état de l’art, en particulier dans le cas de descripteurs binaires de très petite taille (de 32 à 256 bits).Image instance retrieval is the problem of finding an object instance present in a query image from a database of images. Also referred to as particular object retrieval, this problem typically entails determining with high precision whether the retrieved image contains the same object as the query image. Scale, rotation and orientation changes between query and database objects and background clutter pose significant challenges for this problem. State-of-the-art image instance retrieval pipelines consist of two major steps: first, a subset of images similar to the query are retrieved from the database, and second, Geometric Consistency Checks (GCC) are applied to select the relevant images from the subset with high precision. The first step is based on comparison of global image descriptors: high-dimensional vectors with up to tens of thousands of dimensions rep- resenting the image data. The second step is computationally highly complex and can only be applied to hundreds or thousands of images in practical applications. More discriminative global descriptors result in relevant images being more highly ranked, resulting in fewer images that need to be compared pairwise with GCC. As a result, better global descriptors are key to improving retrieval performance and have been the object of much recent interest. Furthermore, fast searches in large databases of millions or even billions of images requires the global descriptors to be compressed into compact representations. This thesis will focus on how to achieve extremely compact global descriptor representations for large-scale image instance retrieval. After introducing background concepts about supervised neural networks, Restricted Boltzmann Machine (RBM) and deep learning in Chapter 2, Chapter 3 will present the design principles and recent work for the Convolutional Neural Networks (CNN), which recently became the method of choice for large-scale image classification tasks. Next, an original multistage approach for the fusion of the output of multiple CNN is proposed. Submitted as part of the ILSVRC 2014 challenge, results show that this approach can significantly improve classification results. The promising perfor- mance of CNN is largely due to their capability to learn appropriate high-level visual representations from the data. Inspired by a stream of recent works showing that the representations learnt on one particular classification task can transfer well to other classification tasks, subsequent chapters will focus on the transferability of representa- tions learnt by CNN to image instance retrieval