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