Vacances de Noël

Vacances de Noël

TBA

11h - salle ACTIA

Fanny Mollandin (INRAE, GABI)

11h - salle ACTIA

TBA

11h - salle ACTIA

TBA

Clément Chadebec (Université de Paris, INRIA, INSERM)

11h - salle ACTIA

Data Augmentation in High Dimensional Low Sample Size Setting with Geometry-Aware Variational Autoencoders

Perrine Lacroix (LMO, IPS2)

11h - salle ACTIA

TBA

Baptiste Kerleguer (CMAP, Ecole polytechinque)

11h - salle ACTIA

TBA

Jour de la Toussaint

Vacances de la Toussaint

Liliane Bel (MIA Paris, AgroParisTech)

11h - salle ACTIA

TBA

Résumé : TBA

Thanh Mai Pham Ngoc (LMO, Université Paris Saclay)

11h - salle ACTIA

Adaptive estimation of nonparametric geometric graphs

Résumé : This talk studies the recovery of graphons when they are convolution kernels on compact (symmetric) metric spaces. This case is of particular interest since it covers the situation where the probability of an edge depends only on some unknown nonparametric function of the distance between latent (non observed) points, referred to as Nonparametric Geometric Graphs (NGG).
In this setting, adaptive estimation of NGG is possible using a spectral procedure combined with a Goldenshluger-Lepski adaptation method. The latent spaces covered by our framework encompass (among others) compact symmetric spaces of rank one, namely real spheres and projective spaces.

Mathis Chagneux (MIA Paris, AgroParisTech)

11h - salle ACTIA

Macrolitter video counting on river banks with state space models for moving cameras

Résumé : Litter is a known cause of degradation in marine environments and most of it travels in rivers before reaching the oceans. In this paper, we present a novel algorithm to assist waste monitoring along watercourses. While several attempts have been made to quantify litter using neural object detection in photographs of floating items, we tackle the more challenging task of counting directly in videos using boat-embedded cameras. We rely on multi-object tracking (MOT) but focus on the key pitfalls of false and redundant counts which arise in typical scenarios of poor detection performance. Our system only requires supervision at the image level and performs Bayesian filtering via a state space model based on optical flow. We present a new open image dataset gathered through a crowdsourced campaign and used to train a center-based anchor-free object detector. Realistic video footage assembled by water monitoring experts is annotated and provided for evaluation. Improvements in count quality are demonstrated against systems built from state-of-the-art multi-object trackers sharing the same detection capabilities. A precise error decomposition allows clear analysis and highlights the remaining challenges.

State of The R

11h - salle ACTIA

Retour de la semaine à Roscoff

Wencan Zhu (AgroParisTech & Sanofi)

11h - salle ACTIA

A variable selection approach for highly correlated predictors in high-dimensional data

Tâm Le Minh (MIA Paris, AgroParisTech)

11h - salle ACTIA

Comparaison de réseaux d'interaction écologiques au moyen de modèles probabilistes échangeables