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cs.CL2025
Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models
Ece Takmaz, Lisa Bylinina, Jakub Dotlacil
State-of-the-art vision-and-language models consist of many parameters and learn from enormous datasets, surpassing the amounts of linguistic data that children are exposed to as t…
cs.CV2025
Correlates of Image Memorability in Vision Encoders: Activations, Attention Entropy, Patch Uniformity and Autoencoder Losses
Ece Takmaz, Albert Gatt, Jakub Dotlacil
Images vary in how memorable they are to humans. Inspired by findings from cognitive science and computer vision, we explore correlates of image memorability in pretrained transfor…
cs.CV2025
Common Objects Out of Context (COOCo): Investigating Multimodal Context and Semantic Scene Violations in Referential Communication
Filippo Merlo, Ece Takmaz, Wenkai Chen +1
To what degree and under what conditions do VLMs rely on scene context when generating references to objects? To address this question, we introduce the $\textit{Common Objects Out…