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Jose J. Guerrero

8 accepted papers

2026

Multimodal Knowledge Distillation for Egocentric Action Recognition Robust to Missing Modalities

Dustin Carrión-Ojeda, Maria Santos-Villafranca, Alejandro Perez-Yus, Jesus Bermudez-Cameo, Jose J. Guerrero, Simone Schaub-Meyer
ICRA 2026poster

Egocentric action recognition enables robots to facilitate human-robot interactions and monitor task progress. Existing methods often rely solely on RGB videos, although additional modalities, such as audio, can improve accuracy under challenging conditions. However, most multimodal approaches assum…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

O-MaMa: Learning Object Mask Matching between Egocentric and Exocentric Views

Lorenzo Mur-Labadia, Maria Santos-Villafranca, Jesus Bermudez-Cameo, Alejandro Perez-Yus, Ruben Martinez-Cantin, Jose J. Guerrero
ICCV 2025poster

Understanding the world from multiple perspectives is essential for intelligent systems operating together, where segmenting common objects across different views remains an open problem. We introduce a new approach that re-defines cross-image segmentation by treating it as a mask matching task. Our…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

AFF-ttention! Affordances and Attention models for Short-Term Object Interaction Anticipation

Lorenzo Mur-Labadia*, Ruben Martinez-Cantin, Jose J Guerrero, Giovanni Maria Farinella, Antonino Furnari
ECCV 2024poster

"Short-Term object-interaction Anticipation (STA) consists of detecting the location of the next-active objects, the noun and verb categories of the interaction, and the time to contact from the observation of egocentric video. This ability is fundamental for wearable assistants or human-robot inter…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2023

Bayesian deep learning for affordance segmentation in images

Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Jose J. Guerrero
ICRA 2023poster

Affordances are a fundamental concept in robotics since they relate available actions for an agent depending on its sensory-motor capabilities and the environment. We present a novel Bayesian deep network to detect affordances in images, at the same time that we quantify the distribution of the alea…

Cited by 13Source ↗Scholar ↗
2023

FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions from Single Panoramas

Bruno Berenguel-Baeta, Jesus Bermudez-Cameo, Jose J. Guerrero
ICRA 2023poster

In this work we present FreDSNet, a deep learning solution which obtains semantic 3D understanding of indoor environments from single panoramas. Omnidirectional images reveal task-specific advantages when addressing scene understanding problems due to the 360-degree contextual information about the…

Cited by 21Source ↗code ↗Scholar ↗
2023

Multi-label Affordance Mapping from Egocentric Vision

Lorenzo Mur-Labadia, Jose J. Guerrero, Ruben Martinez-Cantin
ICCV 2023poster

Accurate affordance detection and segmentation with pixel precision is an important piece in many complex systems based on interactions, such as robots and assitive devices. We present a new approach to affordance perception which enables accurate multi-label segmentation. Our approach can be used t…

Cited by 17PDF ↗code ↗Scholar ↗
2020

Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets

Clara Fernandez-Labrador, Ajad Chhatkuli, Danda Pani Paudel, Jose J. Guerrero, Cédric Demonceaux, Luc Van Gool
ECCV 2020poster

Automatic discovery of category-specific 3D keypoints from a collection of objects of a category is a challenging problem. The difficulty is added when objects are represented by 3D point clouds, with variations in shape and semantic parts and unknown coordinate frames. We define keypoints to be cat…

Cited by 45Source ↗PDF ↗code ↗Scholar ↗
2020

What’s in my Room? Object Recognition on Indoor Panoramic Images

Julia Guerrero-Viu, Clara Fernandez-Labrador, Cédric Demonceaux, Jose J. Guerrero
ICRA 2020poster

In the last few years, there has been a growing interest in taking advantage of the 360° panoramic images potential, while managing the new challenges they imply. While several tasks have been improved thanks to the contextual information these images offer, object recognition in indoor scenes still…

Cited by 38Source ↗Scholar ↗