46 citations · 59 across the 10 of their papers we have counts for
5 papers · 1 filter
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks
Yuyan Zhang, Derya Soydaner, Fatemeh Behrad +2
Deep neural networks perform well in object recognition, but do they perceive objects like humans? This study investigates the Gestalt principle of closure in convolutional neural…
Have Large Vision-Language Models Mastered Art History?
Ombretta Strafforello, Derya Soydaner, Michiel Willems +2
The emergence of large Vision-Language Models (VLMs) has established new baselines in image classification across multiple domains. We examine whether their multimodal reasoning ca…
BackFlip: The Impact of Local and Global Data Augmentations on Artistic Image Aesthetic Assessment
Ombretta Strafforello, Gonzalo Muradas Odriozola, Fatemeh Behrad +4
Assessing the aesthetic quality of artistic images presents unique challenges due to the subjective nature of aesthetics and the complex visual characteristics inherent to artworks…
Finding Closure: A Closer Look at the Gestalt Law of Closure in Convolutional Neural Networks
Yuyan Zhang, Derya Soydaner, Lisa Koßmann +2
The human brain has an inherent ability to fill in gaps to perceive figures as complete wholes, even when parts are missing or fragmented. This phenomenon is known as Closure in ps…
Nuclear mass predictions using machine learning models
Esra Yüksel, Derya Soydaner, Hüseyin Bahtiyar
The exploration of nuclear mass or binding energy, a fundamental property of atomic nuclei, remains at the forefront of nuclear physics research due to limitations in experimental…