2 citations · 2 across the 15 of their papers we have counts for
6 papers · 1 filter
Toddlers' Active Gaze Behavior Supports Self-Supervised Object Learning
Zhengyang Yu, Arthur Aubret, Marcel C. Raabe +3
Toddlers learn to recognize objects from different viewpoints with almost no supervision. During this learning, they execute frequent eye and head movements that shape their visual…
Predictive Coding with Spiking Neural Networks: a Survey
Antony W. N'dri, William Gebhardt, Céline Teulière +4
In this article, we review a class of neuro-mimetic computational models that we place under the label of spiking predictive coding. Specifically, we review the general framework o…
Self-supervised visual learning from interactions with objects
Arthur Aubret, Céline Teulière, Jochen Triesch
Self-supervised learning (SSL) has revolutionized visual representation learning, but has not achieved the robustness of human vision. A reason for this could be that SSL does not…
Learning Object Semantic Similarity with Self-Supervision
Arthur Aubret, Timothy Schaumlöffel, Gemma Roig +1
Humans judge the similarity of two objects not just based on their visual appearance but also based on their semantic relatedness. However, it remains unclear how humans learn abou…
Self-Supervised Learning of Color Constancy
Markus R. Ernst, Francisco M. López, Arthur Aubret +2
Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions. While CC and its…
Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play
Timothy Schaumlöffel, Arthur Aubret, Gemma Roig +1
Infants' ability to recognize and categorize objects develops gradually. The second year of life is marked by both the emergence of more semantic visual representations and a bette…