Florent Chiaroni

Orcid: 0000-0003-0165-923X

According to our database1, Florent Chiaroni authored at least 12 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
MoP-CLIP: A Mixture of Prompt-Tuned CLIP Models for Domain Incremental Learning.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Bag of Tricks for Fully Test-Time Adaptation.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023
Parametric Information Maximization for Generalized Category Discovery.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
Mutual Information-based Generalized Category Discovery.
CoRR, 2022

Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions.
CoRR, 2022

2021
Self-Supervised Learning for Autonomous Vehicles Perception: A Conciliation Between Analytical and Learning Methods.
IEEE Signal Process. Mag., 2021

2020
Counter-examples generation from a positive unlabeled image dataset.
Pattern Recognit., 2020

2019
Self-supervised classification of dynamic obstacles using the temporal information provided by videos.
CoRR, 2019

Generating Relevant Counter-Examples from a Positive Unlabeled Dataset for Image Classification.
CoRR, 2019

Hallucinating A Cleanly Labeled Augmented Dataset from A Noisy Labeled Dataset Using GAN.
Proceedings of the 2019 IEEE International Conference on Image Processing, 2019

2018
Classification d'images en apprenant sur des échantillons positifs et non labélisés avec un réseau antagoniste génératif(Image classification by learning on positive and unlabeled samples with a generative adversarial network).
Proceedings of the Actes de la Conférence Nationale d'Intelligence Artificielle et Rencontres des Jeunes Chercheurs en Intelligence Artificielle (CNIA+RJCIA 2018), 2018

Learning with A Generative Adversarial Network From a Positive Unlabeled Dataset for Image Classification.
Proceedings of the 2018 IEEE International Conference on Image Processing, 2018


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