Élisa Fromont

Kevin Fauvel, Élisa Fromont, Véronique Masson et al.

lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. The package implements Local Cascade Ensemble (LCE), a machine learning method that further enhances the prediction performance of the current state-of-the-art methods Random Forest and XGBoost. LCE combines their strengths and adopts a complementary diversification approach to obtain a better generalizing predictor. The package is compatible with scikit-learn, therefore it can interact with scikit-learn pipelines and model selection tools. It is distributed under the Apache 2.0 license, and its source code is available at https://github.com/LocalCascadeEnsemble/LCE.

Manuel Nkegoum, Minh-Tan Pham, Élisa Fromont et al.

Multispectral object detection is critical for safety-sensitive applications such as autonomous driving and surveillance, where robust perception under diverse illumination conditions is essential. However, the limited availability of annotated multispectral data severely restricts the training of deep detectors. In such data-scarce scenarios, textual class information can serve as a valuable source of semantic supervision. Motivated by the recent success of Vision-Language Models (VLMs) in computer vision, we explore their potential for few-shot multispectral object detection. Specifically, we adapt two representative VLM-based detectors, Grounding DINO and YOLO-World, to handle multispectral inputs and propose an effective mechanism to integrate text, visual and thermal modalities. Through extensive experiments on two popular multispectral image benchmarks, FLIR and M3FD, we demonstrate that VLM-based detectors not only excel in few-shot regimes, significantly outperforming specialized multispectral models trained with comparable data, but also achieve competitive or superior results under fully supervised settings. Our findings reveal that the semantic priors learned by large-scale VLMs effectively transfer to unseen spectral modalities, ofFering a powerful pathway toward data-efficient multispectral perception.

Manuel Nkegoum, Minh-Tan Pham, Élisa Fromont et al.

Few-shot multispectral object detection (FSMOD) addresses the challenge of detecting objects across visible and thermal modalities with minimal annotated data. In this paper, we explore this complex task and introduce a framework named "FSMODNet" that leverages cross-modality feature integration to improve detection performance even with limited labels. By effectively combining the unique strengths of visible and thermal imagery using deformable attention, the proposed method demonstrates robust adaptability in complex illumination and environmental conditions. Experimental results on two public datasets show effective object detection performance in challenging low-data regimes, outperforming several baselines we established from state-of-the-art models. All code, models, and experimental data splits can be found at https://anonymous.4open.science/r/Test-B48D.

Kevin Fauvel, Tao Lin, Véronique Masson et al.

Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets. Moreover, this deep learning approach cannot provide faithful explanations as it relies on post hoc model-agnostic explainability methods, which could prevent its use in numerous applications. In this paper, we present XCM, an eXplainable Convolutional neural network for MTS classification. XCM is a new compact convolutional neural network which extracts information relative to the observed variables and time directly from the input data. Thus, XCM architecture enables a good generalization ability on both large and small datasets, while allowing the full exploitation of a faithful post hoc model-specific explainability method (Gradient-weighted Class Activation Mapping) by precisely identifying the observed variables and timestamps of the input data that are important for predictions. We first show that XCM outperforms the state-of-the-art MTS classifiers on both the large and small public UEA datasets. Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability. Finally, we present how XCM can outperform the current most accurate state-of-the-art algorithm on a real-world application while enhancing explainability by providing faithful and more informative explanations.

Kevin Fauvel, Véronique Masson, Élisa Fromont

Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods. The framework details a set of characteristics that systematize the performance-explainability assessment of existing machine learning methods. In order to illustrate the use of the framework, we apply it to benchmark the current state-of-the-art multivariate time series classifiers.

Kevin Fauvel, Élisa Fromont, Véronique Masson et al.

We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the public UEA datasets. Furthermore, XEM provides faithful explainability-by-design and manifests robust performance when faced with challenges arising from continuous data collection (different MTS length, missing data and noise).