18 citations · 26 across the 8 of their papers we have counts for
8 papers
ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations
Florent Chiaroni, Ali Ayub, Ola Ahmad
In robotics applications, few-shot segmentation is crucial because it allows robots to perform complex tasks with minimal training data, facilitating their adaptation to diverse, r…
Bag of Tricks for Fully Test-Time Adaptation
Saypraseuth Mounsaveng, Florent Chiaroni, Malik Boudiaf +2
Fully Test-Time Adaptation (TTA), which aims at adapting models to data drifts, has recently attracted wide interest. Numerous tricks and techniques have been proposed to ensure ro…
MoP-CLIP: A Mixture of Prompt-Tuned CLIP Models for Domain Incremental Learning
Julien Nicolas, Florent Chiaroni, Imtiaz Ziko +3
Despite the recent progress in incremental learning, addressing catastrophic forgetting under distributional drift is still an open and important problem. Indeed, while state-of-th…
Parametric Information Maximization for Generalized Category Discovery
Florent Chiaroni, Jose Dolz, Ziko Imtiaz Masud +2
We introduce a Parametric Information Maximization (PIM) model for the Generalized Category Discovery (GCD) problem. Specifically, we propose a bi-level optimization formulation, w…
Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions
Florent Chiaroni, Malik Boudiaf, Amar Mitiche +1
We explore clustering the softmax predictions of deep neural networks and introduce a novel probabilistic clustering method, referred to as k-sBetas. In the general context of clus…
Generating Relevant Counter-Examples from a Positive Unlabeled Dataset for Image Classification
Florent Chiaroni, Ghazaleh Khodabandelou, Mohamed-Cherif Rahal +2
With surge of available but unlabeled data, Positive Unlabeled (PU) learning is becoming a thriving challenge. This work deals with this demanding task for which recent GAN-based P…