1 citations · 2 across the 11 of their papers we have counts for
11 papers · 1 filter
On the Role of Individual Differences in Current Approaches to Computational Image Aesthetics
Li-Wei Chen, Ombretta Strafforello, Anne-Sofie Maerten +2
Image aesthetic assessment (IAA) evaluates image aesthetics, a task complicated by image diversity and user subjectivity. Current approaches address this in two stages: Generic IAA…
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…
Aligning Object Detector Bounding Boxes with Human Preference
Ombretta Strafforello, Osman S. Kayhan, Oana Inel +2
Previous work shows that humans tend to prefer large bounding boxes over small bounding boxes with the same IoU. However, we show here that commonly used object detectors predict l…
Do Object Detection Localization Errors Affect Human Performance and Trust?
Sven de Witte, Ombretta Strafforello, Jan van Gemert
Bounding boxes are often used to communicate automatic object detection results to humans, aiding humans in a multitude of tasks. We investigate the relationship between bounding b…
Color Equivariant Convolutional Networks
Attila Lengyel, Ombretta Strafforello, Robert-Jan Bruintjes +2
Color is a crucial visual cue readily exploited by Convolutional Neural Networks (CNNs) for object recognition. However, CNNs struggle if there is data imbalance between color vari…
Can we predict the Most Replayed data of video streaming platforms?
Alessandro Duico, Ombretta Strafforello, Jan van Gemert
Predicting which specific parts of a video users will replay is important for several applications, including targeted advertisement placement on video platforms and assisting vide…
Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models
Jan Warchocki, Teodor Oprescu, Yunhan Wang +6
In temporal action localization, given an input video, the goal is to predict which actions it contains, where they begin, and where they end. Training and testing current state-of…
Video BagNet: short temporal receptive fields increase robustness in long-term action recognition
Ombretta Strafforello, Xin Liu, Klamer Schutte +1
Previous work on long-term video action recognition relies on deep 3D-convolutional models that have a large temporal receptive field (RF). We argue that these models are not alway…
Are current long-term video understanding datasets long-term?
Ombretta Strafforello, Klamer Schutte, Jan van Gemert
Many real-world applications, from sport analysis to surveillance, benefit from automatic long-term action recognition. In the current deep learning paradigm for automatic action r…
Humans disagree with the IoU for measuring object detector localization error
Ombretta Strafforello, Vanathi Rajasekart, Osman S. Kayhan +2
The localization quality of automatic object detectors is typically evaluated by the Intersection over Union (IoU) score. In this work, we show that humans have a different view on…