176 citations · 274 across the 36 of their papers we have counts for
44 papers
LVMT: Video Mask Transformer for Long-term Video Segmentation
Narges Norouzi, Niccol`o Cavagnero, Niccolò Cavagnero +4
Existing online video segmentation methods struggle to track objects in long, complex videos with long-term occlusions. We hypothesize that this limitation is caused by (i) the ina…
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…
Towards Data-Efficient Video Pre-training with Frozen Image Foundation Models
Svetlana Orlova, Niccolò Cavagnero, Gijs Dubbelman
Video foundation models achieve strong performance across many video understanding tasks, but typically require large-scale pre-training on massive video datasets, resulting in sub…
REFNet++: Multi-Task Efficient Fusion of Camera and Radar Sensor Data in Bird's-Eye Polar View
Kavin Chandrasekaran, Sorin Grigorescu, Gijs Dubbelman +1
A realistic view of the vehicle's surroundings is generally offered by camera sensors, which is crucial for environmental perception. Affordable radar sensors, on the other hand, a…
Revisiting Radar Perception With Spectral Point Clouds
Hamza Alsharif, Jing Gu, Pavol Jancura +2
Radar perception models are trained with different inputs, from range-Doppler spectra to sparse point clouds. Dense spectra are assumed to outperform sparse point clouds, yet they…
Orion-Lite: Distilling LLM Reasoning into Efficient Vision-Only Driving Models
Jing Gu, Niccolò Cavagnero, Gijs Dubbelman
Leveraging the general world knowledge of Large Language Models (LLMs) holds significant promise for improving the ability of autonomous driving systems to handle rare and complex…
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…
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…
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…
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…
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…
A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data
Kavin Chandrasekaran, Sorin Grigorescu, Gijs Dubbelman +1
Cameras can be used to perceive the environment around the vehicle, while affordable radar sensors are popular in autonomous driving systems as they can withstand adverse weather c…
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…
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…