activity
20162026
most citedFind the Cliffhanger: Multi-Modal Trailerness in Soap Operas

2 citations · 7 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Gender Artifacts from Art History to Text-to-Image Generation

Piera Riccio, Miriam Doh, Benedikt Höltgen +2

Artistic styles are rooted in specific socio-historical contexts that encode social hierarchies, including distinct constructions of gender. Yet in AI research, style has long been…

cs.CV2024★ 2 cited

Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas

Carlo Bretti, Pascal Mettes, Hendrik Vincent Koops +2

Creating a trailer requires carefully picking out and piecing together brief enticing moments out of a longer video, making it a challenging and time-consuming task. This requires…

cs.CV2023★ 1 cited

Blind Dates: Examining the Expression of Temporality in Historical Photographs

Alexandra Barancová, Melvin Wevers, Nanne van Noord

This paper explores the capacity of computer vision models to discern temporal information in visual content, focusing specifically on historical photographs. We investigate the da…

cs.CV2023★ 1 cited

Prototype-based Dataset Comparison

Nanne van Noord

Dataset summarisation is a fruitful approach to dataset inspection. However, when applied to a single dataset the discovery of visual concepts is restricted to those most prominent…

cs.CV2023★ 2 cited

Hierarchical Explanations for Video Action Recognition

Sadaf Gulshad, Teng Long, Nanne van Noord

To interpret deep neural networks, one main approach is to dissect the visual input and find the prototypical parts responsible for the classification. However, existing methods of…

cs.CV2016★ 1 cited

Learning scale-variant and scale-invariant features for deep image classification

Nanne van Noord, Eric Postma

Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted,…