1 citations · 1 across the 6 of their papers we have counts for
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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…