1 citations · 1 across the 4 of their papers we have counts for
5 papers
A Controlled Study of Self-Supervised Image and Video Pretraining under Limited Resources
Brunó B. Englert, Gijs Dubbelman
Visual foundation models are a cornerstone of image and video understanding but typically require large amounts of data and computation. The current scale required for pretraining…
Simplifying Traffic Anomaly Detection with Video Foundation Models
Svetlana Orlova, Tommie Kerssies, Brunó B. Englert +1
Recent methods for ego-centric Traffic Anomaly Detection (TAD) often rely on complex multi-stage or multi-representation fusion architectures, yet it remains unclear whether such c…
What is the Added Value of UDA in the VFM Era?
Brunó B. Englert, Tommie Kerssies, Gijs Dubbelman
Unsupervised Domain Adaptation (UDA) can improve a perception model's generalization to an unlabeled target domain starting from a labeled source domain. UDA using Vision Foundatio…
VFM-UDA++: Improving Network Architectures and Data Strategies for Unsupervised Domain Adaptive Semantic Segmentation
Brunó B. Englert, Gijs Dubbelman
Unsupervised Domain Adaptation (UDA) enables strong generalization from a labeled source domain to an unlabeled target domain, often with limited data. In parallel, Vision Foundati…
Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation
Brunó B. Englert, Fabrizio J. Piva, Tommie Kerssies +2
Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safety-critical applications, where d…