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20242026
most citedMAPS: A Synthetic Dataset for Probing Vision Models in a Controlled 3D Scene Space

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cs.CV2026

Mind the Student: Behavioral and Contextual Cues for Automated Engagement Prediction in Online Learning

Alperen Kantarci, Visvanathan Ramesh, Gemma Roig

The prediction of student engagement from the online tutoring videos is difficult because engagement is a multidimensional construct comprising distinct behavioral, emotional, and…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…