activity
20192026
most citedTransformer-Based Self-Supervised Learning for Emotion Recognition

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

eess.SP2026

A Transformer for Joint Multi-Receiver Pilotless Wi-Fi Decoding

Xavier Tardy, Grégoire Lefebvre, Apostolos Kountouris +2

In this paper we discuss the development of a fully pilotless multi-access-point Wi-Fi receiver. This is based on a self-attention Transformer that operates on per-(access point, s…

cs.NI2026

Intent-Based Orchestration in Open RAN: An ns-3 Simulation Framework

Pouya Agheli, Grégoire Lefebvre

This paper presents an extensible ns-3-based simulation framework for evaluating intent-based, semantics-aware control in Open RAN architectures. The framework integrates external…

eess.SP2026

Scalable Cross-Attention Transformer for Cooperative Multi-AP OFDM Uplink Reception

Xavier Tardy, Grégoire Lefebvre, Apostolos Kountouris +2

We propose a cross-attention Transformer for joint decoding of uplink OFDM signals received by multiple coordinated access points. A shared per-receiver encoder learns the time-fre…

cs.LG2023

Accommodating Missing Modalities in Time-Continuous Multimodal Emotion Recognition

Juan Vazquez-Rodriguez, Grégoire Lefebvre, Julien Cumin +1

Decades of research indicate that emotion recognition is more effective when drawing information from multiple modalities. But what if some modalities are sometimes missing? To add…

eess.SP2022

Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals

Juan Vazquez-Rodriguez, Grégoire Lefebvre, Julien Cumin +1

In this paper, we address the problem of multimodal emotion recognition from multiple physiological signals. We demonstrate that a Transformer-based approach is suitable for this t…

q-bio.NC2022★ 1 cited

Transformer-Based Self-Supervised Learning for Emotion Recognition

Juan Vazquez-Rodriguez, Grégoire Lefebvre, Julien Cumin +1

In order to exploit representations of time-series signals, such as physiological signals, it is essential that these representations capture relevant information from the whole si…