Predicting Intentions of Pedestrians from 2D Skeletal Pose Sequences with a Representation-Focused Multi-Branch Deep Learning Network. - Joseph Gesnouin; Steve Pechberti; Guillaume Bresson; Bogdan Stanciulescu; Fabien Moutarde (Algorithms, 2020)

TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction. - Joseph Gesnouin; Steve Pechberti; Bogdan Stanciulescu; Fabien Moutarde (FG, 2021)

Asymmetrical Bi-RNN for pedestrian trajectory encoding. - Raphaël Rozenberg; Joseph Gesnouin; Fabien Moutarde (CoRR, 2021)

Assessing Cross-dataset Generalization of Pedestrian Crossing Predictors. - Joseph Gesnouin; Steve Pechberti; Bogdan Stanciulescu; Fabien Moutarde (IV, 2022)

Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions. (Analyse des mouvements et gestes des piétons via caméra embarquée pour la prédiction de leurs intentions). - Joseph Gesnouin (2022)

LLaMandement: Large Language Models for Summarization of French Legislative Proposals. - Joseph Gesnouin; Yannis Tannier; Christophe Gomes Da Silva; Hatim Tapory; Camille Brier; Hugo Simon; Raphaël Rozenberg; Hermann Woehrel; Mehdi El Yakaabi; Thomas Binder; Guillaume Marie; Emilie Caron; et al. (CoRR, 2024)

GraphRAG: Leveraging Graph-Based Efficiency to Minimize Hallucinations in LLM-Driven RAG for Finance Data. - Mariam Barry; Gaëtan Caillaut; Pierre Halftermeyer; Raheel Qader; Mehdi Mouayad; Fabrice Le Deit; Dimitri Cariolaro; Joseph Gesnouin (COLING Workshops, 2025)