69 citations · 80 across the 25 of their papers we have counts for
7 papers · 1 filter
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
Tuan-Hung Vu, Eduardo Valle, Andrei Bursuc +16
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. W…
First Place Solution to the ECCV 2024 BRAVO Challenge: Evaluating Robustness of Vision Foundation Models for Semantic Segmentation
Tommie Kerssies, Daan de Geus, Gijs Dubbelman
In this report, we present the first place solution to the ECCV 2024 BRAVO Challenge, where a model is trained on Cityscapes and its robustness is evaluated on several out-of-distr…
Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Gonzalo Martin Garcia, Karim Knaebel, Christian Schmidt +3
Recent work showed that large diffusion models can be reused as highly precise monocular depth estimators by casting depth estimation as an image-conditional image generation task.…
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…
How to Benchmark Vision Foundation Models for Semantic Segmentation?
Tommie Kerssies, Daan de Geus, Gijs Dubbelman
Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. B…