2 citations · 3 across the 15 of their papers we have counts for
16 papers
Your Self-Supervised Projection Head Captures Object Co-Occurrence Statistics
Arthur Aubret, Jochen Triesch, Céline Teulière
Self-Supervised Learning (SSL) has achieved impressive success in learning semantic visual representations, yet the underlying principles driving this success remain underexplored.…
Temporal Slowness in Central Vision Drives Semantic Object Learning
Timothy Schaumlöffel, Arthur Aubret, Gemma Roig +1
Humans acquire semantic object representations from egocentric visual streams with minimal supervision, but the underlying mechanisms remain unclear. Importantly, the visual system…
Re-assessing the evidence for mental rotation abilities in children using computational models
Arthur Aubret, Jochen Triesch
There is strong and diverse evidence for mental rotation (MR) abilities in adults. However, current evidence for MR in children rests on just a few behavioral paradigms adapted fro…
Simulated Cortical Magnification Supports Self-Supervised Object Learning
Zhengyang Yu, Arthur Aubret, Chen Yu +1
Recent self-supervised learning models simulate the development of semantic object representations by training on visual experience similar to that of toddlers. However, these mode…
MIMo grows! Simulating body and sensory development in a multimodal infant model
Francisco M. López, Miles Lenz, Marco G. Fedozzi +2
Infancy is characterized by rapid body growth and an explosive change of sensory and motor abilities. However, developmental robots and simulation platforms are typically designed…
Human Gaze Boosts Object-Centered Representation Learning
Timothy Schaumlöffel, Arthur Aubret, Gemma Roig +1
Recent self-supervised learning (SSL) models trained on human-like egocentric visual inputs substantially underperform on image recognition tasks compared to humans. These models t…
Seeing the Whole in the Parts in Self-Supervised Representation Learning
Arthur Aubret, Céline Teulière, Jochen Triesch
Recent successes in self-supervised learning (SSL) model spatial co-occurrences of visual features either by masking portions of an image or by aggressively cropping it. Here, we p…
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…
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…
MIMo: A Multi-Modal Infant Model for Studying Cognitive Development
Dominik Mattern, Pierre Schumacher, Francisco M. López +4
Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the…
Time to augment self-supervised visual representation learning
Arthur Aubret, Markus Ernst, Céline Teulière +1
Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, self-supervised learning (SSL) has led to majo…
Embodied vision for learning object representations
Arthur Aubret, Céline Teulière, Jochen Triesch
Recent time-contrastive learning approaches manage to learn invariant object representations without supervision. This is achieved by mapping successive views of an object onto clo…
DisTop: Discovering a Topological representation to learn diverse and rewarding skills
Arthur Aubret, Laetitia matignon, Salima Hassas
The optimal way for a deep reinforcement learning (DRL) agent to explore is to learn a set of skills that achieves a uniform distribution of states. Following this,we introduce Dis…