69 citations · 80 across the 25 of their papers we have counts for
30 papers
Towards Metric-Agnostic Trajectory Forecasting
Markus Knoche, Daan de Geus, Bastian Leibe
Accurate trajectory forecasting of surrounding traffic participants is a core capability for autonomous driving, enabling vehicles to anticipate behavior and plan safe maneuvers. W…
Surgical Anatomy Recognition with Context Learning using Foundation Representations
Ronald L. P. D. de Jong, Tim J. M. Jaspers, Raf A. H. Vervoort +9
Accurate recognition of anatomical structures is essential for safe and effective minimally invasive surgery (MIS), yet it remains underexplored in surgical computer vision due to…
SurGe: Improved Surface Geometry in Point Maps
Karim Knaebel, Gonzalo Martin Garcia, Christian Schmidt +4
Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still exhibit inaccurate local surface g…
A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens
Tommie Kerssies, Gabriele Berton, Ju He +5
Anticipating diverse future states is a central challenge in video world modeling. Discriminative world models produce a deterministic prediction that implicitly averages over poss…
Volume Transformer: Revisiting Vanilla Transformers for 3D Scene Understanding
Kadir Yilmaz, Adrian Kruse, Tristan Höfer +2
Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong domain priors. This isolates the fie…
PMT: Plain Mask Transformer for Image and Video Segmentation with Frozen Vision Encoders
Niccolò Cavagnero, Narges Norouzi, Gijs Dubbelman +1
Vision Foundation Models (VFMs) pre-trained at scale enable a single frozen encoder to serve multiple downstream tasks simultaneously. Recent VFM-based encoder-only models for imag…
VidEoMT: Your ViT is Secretly Also a Video Segmentation Model
Narges Norouzi, Idil Esen Zulfikar, Niccolò Cavagnero +4
Existing online video segmentation models typically combine a per-frame segmenter with complex specialized tracking modules. While effective, these modules introduce significant ar…
LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation
Chang Liu, Henghui Ding, Kaining Ying +46
This report presents an overview of the 7th Large-scale Video Object Segmentation (LSVOS) Challenge held in conjunction with ICCV 2025. Besides the two traditional tracks of LSVOS…
Sa2VA-i: Improving Sa2VA Results with Consistent Training and Inference
Alexey Nekrasov, Ali Athar, Daan de Geus +2
Sa2VA is a recent model for language-guided dense grounding in images and video that achieves state-of-the-art results on multiple segmentation benchmarks and that has become widel…
DONUT: A Decoder-Only Model for Trajectory Prediction
Markus Knoche, Daan de Geus, Bastian Leibe
Predicting the motion of other agents in a scene is highly relevant for autonomous driving, as it allows a self-driving car to anticipate. Inspired by the success of decoder-only m…
How Important are Videos for Training Video LLMs?
George Lydakis, Alexander Hermans, Ali Athar +2
Research into Video Large Language Models (LLMs) has progressed rapidly, with numerous models and benchmarks emerging in just a few years. Typically, these models are initialized w…
Your ViT is Secretly an Image Segmentation Model
Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans +5
Vision Transformers (ViTs) have shown remarkable performance and scalability across various computer vision tasks. To apply single-scale ViTs to image segmentation, existing method…
DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation
Karim Knaebel, Kadir Yilmaz, Daan de Geus +4
Vision foundation models (VFMs) trained on large-scale image datasets provide high-quality features that have significantly advanced 2D visual recognition. However, their potential…
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…
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…
Coordinating Fully-Cooperative Agents Using Hierarchical Learning Anticipation
Ariyan Bighashdel, Daan de Geus, Pavol Jancura +1
Learning anticipation is a reasoning paradigm in multi-agent reinforcement learning, where agents, during learning, consider the anticipated learning of other agents. There has bee…