4 papers
Evaluation of Randomization through Style Transfer for Enhanced Domain Generalization
Dustin Eisenhardt, Timothy Schaumlöffel, Alperen Kantarci +1
Deep learning models for computer vision often suffer from poor generalization when deployed in real-world settings, especially when trained on synthetic data due to the well-known…
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…
Contextual inference from single objects in Vision-Language models
Martina G. Vilas, Timothy Schaumlöffel, Gemma Roig
How much scene context a single object carries is a well-studied question in human scene perception, yet how this capacity is organized in vision-language models (VLMs) remains poo…
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…