3 citations · 6 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets
Marianna Nezhurina, Tomer Porian, Giovanni Pucceti +4
In studies of transferable learning, scaling laws are obtained for various important foundation models to predict their properties and performance at larger scales. We show here ho…
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…
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…
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…
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…
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…
Neural Architecture Search for Visual Anomaly Segmentation
Tommie Kerssies, Joaquin Vanschoren
This paper presents the first application of neural architecture search to the complex task of segmenting visual anomalies. Measurement of anomaly segmentation performance is chall…
Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts
Tommie Kerssies, Mert Kılıçkaya, Joaquin Vanschoren
In this paper, our goal is to adapt a pre-trained convolutional neural network to domain shifts at test time. We do so continually with the incoming stream of test batches, without…