5 citations · 7 across the 6 of their papers we have counts for
9 papers
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…
Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations
Daan de Geus, Gijs Dubbelman
Part-aware panoptic segmentation (PPS) requires (a) that each foreground object and background region in an image is segmented and classified, and (b) that all parts within foregro…
ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain Vision Transformers
Narges Norouzi, Svetlana Orlova, Daan de Geus +1
This work presents Adaptive Local-then-Global Merging (ALGM), a token reduction method for semantic segmentation networks that use plain Vision Transformers. ALGM merges tokens in…
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers
Chenyang Lu, Daan de Geus, Gijs Dubbelman
This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use Vision Tra…
Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images
Daan de Geus, Gijs Dubbelman
Unified panoptic segmentation methods are achieving state-of-the-art results on several datasets. To achieve these results on high-resolution datasets, these methods apply crop-bas…
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning
Ariyan Bighashdel, Daan de Geus, Pavol Jancura +1
Learning anticipation in Multi-Agent Reinforcement Learning (MARL) is a reasoning paradigm where agents anticipate the learning steps of other agents to improve cooperation among t…