1 citations · 1 across the 10 of their papers we have counts for
11 papers
A Probabilistic Perspective on Wasserstein-Based Evidential Uncertainty for Out-of-Distribution Segmentation
Arnold Brosch, Abdelrahman Eldesokey, Michael Felsberg +1
Semantic segmentation networks operate on a fixed set of classes and therefore fail when out-of-distribution (OOD) objects appear during deployment, a critical limitation for safet…
Commutator-Induced Uncertainty in VAEs
Tahereh Dehdarirad, Michael Felsberg, Gabriel Eilertsen +1
Variational autoencoders (VAEs) often struggle to represent non-commutative structure in learned latent spaces. Symmetry-aware VAEs commonly address this issue by enforcing commuta…
SymADiT: Symmetry-Aware Crystal Generation with Latent Flow Matching
Anmar Karmush, Cedric Mathieu Brandenburg, Soheil Ershadrad +4
Tackling the task of crystal generation, we aim to enhance the previously proposed, symmetry-agnostic, All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a symmetry-awar…
HMR-Net: Hierarchical Modular Routing for Cross-Domain Object Detection in Aerial Images
Pourya Shamsolmoali, Masoumeh Zareapoor, Michael Felsberg +3
Despite advances in object detection, aerial imagery remains a challenging domain, as models often fail to generalize across variations in spatial resolution, scene composition, an…
DisasterInsight: A Building-Centric Benchmark for Evaluating Vision--Language Models in Disaster Response
Sara Tehrani, Yonghao Xu, Leif Haglund +3
Vision--language models (VLMs) show promise for disaster-response remote sensing, but existing benchmarks mainly emphasize scene-level or damage-centric assessment. To study this b…
VLM2GeoVec: Toward Universal Multimodal Embeddings for Remote Sensing
Emanuel Sánchez Aimar, Gulnaz Zhambulova, Fahad Shahbaz Khan +2
Satellite imagery differs from natural images in viewpoint, resolution, scale variation, and the prevalence of small objects -- demanding both region-level spatial reasoning and ho…
Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty
Arnold Brosch, Abdelrahman Eldesokey, Michael Felsberg +1
Deep neural networks achieve superior performance in semantic segmentation, but are limited to a predefined set of classes, which leads to failures when they encounter unknown obje…
R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation
William Ljungbergh, Bernardo Taveira, Wenzhao Zheng +8
Validating autonomous driving (AD) systems requires diverse and safety-critical testing, making photorealistic virtual environments essential. Traditional simulation platforms, whi…
A Culturally-diverse Multilingual Multimodal Video Benchmark & Model
Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz +26
Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in Englis…
From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion
Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +3
Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have…
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…