PICNIQ: Pairwise Comparisons for Natural Image Quality Assessment

Blind image quality assessment (BIQA) approaches, while promising for automating image quality evaluation, often fall short in real-world scenarios due to their reliance on a generic quality standard applied uniformly across diverse images. This one-size-fits-all approach overlooks the crucial perceptual relationship between image content and quality, leading to a 'domain shift' challenge where a single quality metric inadequately represents various content types. Furthermore, BIQA techniques typically overlook the inherent differences in the human visual system among different observers. In response to these challenges, this paper introduces PICNIQ, an innovative pairwise comparison framework designed to bypass the limitations of conventional BIQA by emphasizing relative, rather than absolute, quality assessment. PICNIQ is specifically designed to assess the quality differences between image pairs. The proposed framework implements a carefully crafted deep learning architecture, a specialized loss function, and a training strategy optimized for sparse comparison settings. By employing psychometric scaling algorithms like TrueSkill, PICNIQ transforms pairwise comparisons into just-objectionable-difference (JOD) quality scores, offering a granular and interpretable measure of image quality. We conduct our research using comparison matrices from the PIQ23 dataset, which are published in this paper. Our extensive experimental analysis showcases PICNIQ's broad applicability and superior performance over existing models, highlighting its potential to set new standards in the field of BIQA

A nonsmooth Frank–Wolfe algorithm through a dual cutting-plane approach

An extension of the Frank–Wolfe Algorithm (FWA), also known as Conditional Gradient algorithm, is proposed. In its standard form, the FWA allows to solve constrained optimization problems involving $\beta$-smooth cost functions, calling at each iteration a Linear Minimization Oracle. More specifically, the oracle solves a problem obtained by linearization of the original cost function. The algorithm designed and investigated in this article, named Dualized Level-Set (DLS) algorithm, extends the FWA and allows to address a class of nonsmooth costs, involving in particular support functions. The key idea behind the construction of the DLS method is a general interpretation of the FWA as a cutting-plane algorithm, from the dual point of view. The DLS algorithm essentially results from a dualization of a specific cutting-plane algorithm, based on projections on some level sets. The DLS algorithm generates a sequence of primal-dual candidates, and we prove that the corresponding primal-dual gap converges with a rate of $O(1/\sqrt{t})$

Visual channel facilitates the comprehension of the intonation of Brazilian Portuguese wh-questions and wh-exclamations: evidence from congruent and incongruent stimuli

International audienceThis paper presents an audiovisual perceptual analysis of the wh-question and wh-exclamation intonation in Brazilian Portuguese using auditory–visual congruent and incongruent stimuli, to investigate the relative importance of each modality in signaling pragmatic meanings. Ten Brazilian Portuguese speakers (five female) were filmed while producing both speech acts 10 times. Next, artificial stimuli were created: audio and visual cues were either matched (audio and video from the same speech act) or mismatched (audio and video from the different speech acts), resulting in 10 congruent and 10 incongruent stimuli of the wh-questions and the wh-exclamations. The perceptual experiment was taken by 36 Brazilians who identified the stimulus as a question or an exclamation. Results from the logistic regression showed that the factor ‘congruence’ was significant and had a significant interaction with ‘speakers’, which means that the congruent stimuli increased the comprehension of the Brazilian Portuguese wh-questions and wh-exclamations. In contrast, the incongruent stimuli tended to lower listeners’ identification, but to a degree depending on individual speakers’ strategies. Although variation in the accuracy of expressing both speech acts was also found across speakers, this study corroborates that the visual channel impacts the perceptual identification of the pragmatic intonation function of distinguishing sentence mode

Large language models help computer programs to evolve

International audienceA branch of computer science known as genetic programming has been given a boost with the application of large language models that are trained on the combined intuition of the world’s programmers

Liveness Properties in Geometric Logic for Domain-Theoretic Streams

National audienceWe devise a version of Linear Temporal Logic (LTL) on a denotational domain of streams. We investigate this logic in terms of domain theory, (point-free) topology and geometric logic. This yields the first steps toward an extension of the "Domain Theory in Logical Form" paradigm to temporal liveness properties. We show that the negation-free formulae of LTL induce sober subspaces of streams, but that this is in general not the case in presence of negation. We propose a direct, inductive, translation of negation-free LTL to geometric logic. This translation reflects the approximations used to compute the usual fixpoint representations of LTL modalities. As a motivating example, we handle a natural input-output specification for the usual filter function on streams

Marcelle

Marcelle is a modular open source toolkit for programming interactive machine learning applications. Marcelle is built around components embedding computation and interaction that can be composed to form reactive machine learning pipelines and custom user interfaces. This architecture enables rapid prototyping and extension. Marcelle can be used to build interfaces to Python scripts, and it provides flexible data stores to facilitate collaboration between machine learning experts, designers and end users.Marcelle est une boîte à outils modulaire open source pour la programmation d'applications d'apprentissage automatique interactif. Marcelle est construite autour de composants intégrant le calcul et l'interaction qui peuvent être composés pour former des pipelines d'apprentissage machine réactifs et des interfaces utilisateur personnalisées. Marcelle peut être utilisé pour construire des interfaces avec des scripts Python, et il fournit des magasins de données flexibles pour faciliter la collaboration entre les experts en apprentissage automatique, les concepteurs et les utilisateurs finaux

A Novel Closed-Form Approach for Enhancing Efficiency in Pose Estimation from 3D Correspondences

International audienceDetermining the pose using 3D data captured from two distinct frames holds significant significance in various robotic applications such as odometry, simultaneous localization and mapping and place recognition. Typically, this pose is calculated through the resolution of a least-squares problem, which involves establishing correspondences between points, points and planes, or points and lines. This non-linear least-squares problem can be addressed either through iterative optimization or, with greater efficiency, through closed-form solutions achieved via polynomial system solvers. In this paper, the focus is on enhancing the computational efficiency of polynomial solvers utilizing resultants. The newly proposed methodology outperforms previous techniques in terms of speed, all while maintaining identical levels of accuracy and robustness. Through simulations and real-world experiments, we validate the superior precision and robustness of the proposed algorithm when compared with prior methods

Signature-based validation of real-world economic scenarios

Motivated by insurance applications, we propose a new approach for the validation of real-world economic scenarios. This approach is based on the statistical test developed by Chevyrev and Oberhauser (2022) and relies on the notions of signature and maximum mean distance. This test allows to check whether two samples of stochastic processes paths come from the same distribution. Our contribution is to apply this test to a variety of stochastic processes exhibiting different pathwise properties (Hölder regularity, autocorrelation, regime switches) and which are relevant for the modelling of stock prices and stock volatility as well as of inflation in view of actuarial applications

Supervised Contamination Detection, with Flow Cytometry Application

The contamination detection problem aims to determine whether a set of observations has been contaminated, i.e. whether it contains points drawn from a distribution different from the reference distribution. Here, we consider a supervised problem, where labeled samples drawn from both the reference distribution and the contamination distribution are available at training time. This problem is motivated by the detection of rare cells in flow cytometry. Compared to novelty detection problems or two-sample testing, where only samples from the reference distribution are available, the challenge lies in efficiently leveraging the observations from the contamination detection to design more powerful tests. In this article, we introduce a test for the supervised contamination detection problem. We provide non-asymptotic guarantees on its Type I error, and characterize its detection rate. The test relies on estimating reference and contamination densities using histograms, and its power depends strongly on the choice of the corresponding partition. We present an algorithm for judiciously choosing the partition that results in a powerful test. Simulations illustrate the good empirical performances of our partition selection algorithm and the efficiency of our test. Finally, we showcase our method and apply it to a real flow cytometry dataset

Unveiling Strengths and Weaknesses of NLP Systems Based on a Rich Evaluation Corpus: the Case of NER in French

International audienceNamed Entity Recognition (NER) is an applicative task for which annotation schemes vary. To compare the performance of systems which tagsets differ in precision and coverage, it is necessary to assess (i) the comparability of their annotation schemes and (ii) the individual adequacy of the latter to a common annotation scheme. What is more, and given the lack of robustness of some tools towards textual variation, we cannot expect an evaluation led on an homogeneous corpus with low-coverage to provide a reliable prediction of the actual tools performance.To tackle both these limitations in evaluation, we provide a gold corpus for French covering 6 textual genres and annotated with a rich tagset that enables comparison with multiple annotation schemes. We use the flexibility of this gold corpus to provide both: (i) an individual evaluation of four heterogeneous NER systems on their target tagsets, (ii) a comparison of their performance on a common scheme. This rich evaluation framework enables a fair comparison of NER systems across textual genres and annotation schemes