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Luis J. Manso

3 accepted papers

2026

GazeMoE: Perception of Gaze Target with Mixture-Of-Experts

Zhuangzhuang Dai, Zhongxi Lu, Vincent Gbouna Zakka, Luis J. Manso, Jose Maria Alcaraz Calero, Chen Li
ICRA 2026poster

Estimating human gaze target from visible images is a critical task for robots to understand human attention, yet the development of generalizable neural architectures and training paradigms remains challenging. While recent advances in pre-trained vision foundation models offer promising avenues fo…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

GazeTarget360: Towards Gaze Target Estimation in 360-Degree for Robot Perception

Zhuangzhuang Dai, Vincent Gbouna Zakka, Luis J. Manso, Chen Li
IROS 2025

Enabling robots to understand human gaze target is a crucial step to allow capabilities in downstream tasks, for example, attention estimation and movement anticipation in real-world human-robot interactions. Prior works have addressed the in-frame target localization problem with data-driven approa

Cited by 1Source ↗code ↗Scholar ↗
2023

Self-supervised Monocular Depth Estimation: Let's Talk About The Weather

Kieran Saunders, George Vogiatzis, Luis J. Manso
ICCV 2023poster

Current, self-supervised depth estimation architectures rely on clear and sunny weather scenes to train deep neural networks. However, in many locations, this assumption is too strong. For example in the UK (2021), 149 days consisted of rain. For these architectures to be effective in real-world app…

Cited by 47PDF ↗code ↗Scholar ↗