1 citations · 1 across the 9 of their papers we have counts for
12 papers
Uncertainty Estimation in Instance Segmentation of Affordances via Bayesian Visual Transformers
Lorenzo Mur-Labadia, Ruben Martinez-Cantina, Jose J. Guerrero
Visual affordances identify regions in an image with potential interactions, offering a novel paradigm for scene understanding. Recognizing affordances allows autonomous robots to…
V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
Lorenzo Mur-Labadia, Matthew Muckley, Amir Bar +6
We present V-JEPA 2.1, a family of self-supervised models that learn dense, high-quality visual representations for both images and videos while retaining strong global scene under…
Integrating Affordances and Attention models for Short-Term Object Interaction Anticipation
Lorenzo Mur Labadia, Ruben Martinez-Cantin, Jose J. Guerrero +2
Short Term object-interaction Anticipation consists in detecting the location of the next active objects, the noun and verb categories of the interaction, as well as the time to co…
O-MaMa: Learning Object Mask Matching between Egocentric and Exocentric Views
Lorenzo Mur-Labadia, Maria Santos-Villafranca, Jesus Bermudez-Cameo +3
Understanding the world from multiple perspectives is essential for intelligent systems operating together, where segmenting common objects across different views remains an open p…
DIV-FF: Dynamic Image-Video Feature Fields For Environment Understanding in Egocentric Videos
Lorenzo Mur-Labadia, Josechu Guerrero, Ruben Martinez-Cantin
Environment understanding in egocentric videos is an important step for applications like robotics, augmented reality and assistive technologies. These videos are characterized by…
ZARRIO @ Ego4D Short Term Object Interaction Anticipation Challenge: Leveraging Affordances and Attention-based models for STA
Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Josechu Guerrero-Campo +1
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 con…
CARLOR @ Ego4D Step Grounding Challenge: Bayesian temporal-order priors for test time refinement
Carlos Plou, Lorenzo Mur-Labadia, Ruben Martinez-Cantin +1
The goal of the Step Grounding task is to locate temporal boundaries of activities based on natural language descriptions. This technical report introduces a Bayesian-VSLNet to add…
AFF-ttention! Affordances and Attention models for Short-Term Object Interaction Anticipation
Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Josechu Guerrero +2
Short-Term object-interaction Anticipation consists of detecting the location of the next-active objects, the noun and verb categories of the interaction, and the time to contact f…
Multi-label affordance mapping from egocentric vision
Lorenzo Mur-Labadia, Jose J. Guerrero, Ruben Martinez-Cantin
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 pre…
Bayesian Deep Learning for Affordance Segmentation in images
Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Jose J. Guerrero
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 nov…
Robust Fusion for Bayesian Semantic Mapping
David Morilla-Cabello, Lorenzo Mur-Labadia, Ruben Martinez-Cantin +1
The integration of semantic information in a map allows robots to understand better their environment and make high-level decisions. In the last few years, neural networks have sho…
Bayesian deep learning of affordances from RGB images
Lorenzo Mur-Labadia, Ruben Martinez-Cantin
Autonomous agents, such as robots or intelligent devices, need to understand how to interact with objects and its environment. Affordances are defined as the relationships between…