14 citations · 32 across the 21 of their papers we have counts for
9 papers · 1 filter
Orbis 2: A Hierarchical World Model for Driving
Sudhanshu Mittal, Arian Mousakhan, Silvio Galesso +4
Current world models operate at a single level of abstraction, with most prioritizing perceptual fidelity while lacking the spatial reasoning and semantic understanding required fo…
The Surprising Effectiveness of Canonical Knowledge Distillation for Semantic Segmentation
Muhammad Ali, Kevin Alexander Laube, Madan Ravi Ganesh +3
Recent knowledge distillation (KD) methods for semantic segmentation introduce increasingly complex hand-crafted objectives, yet are typically evaluated under fixed iteration sched…
What Drives Compositional Generalization in Visual Generative Models? The Importance of Continuous Training Objectives
Karim Farid, Rajat Sahay, Yumna Ali Alnaggar +4
Compositional generalization, the ability to generate novel combinations of known concepts, is a key ingredient for visual generative models. Yet, not all mechanisms that enable or…
Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models
Arian Mousakhan, Sudhanshu Mittal, Silvio Galesso +2
Existing world models for autonomous driving struggle with long-horizon generation and generalization to challenging scenarios. In this work, we develop a model using simple design…
Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models
Simon Schrodi, David T. Hoffmann, Max Argus +2
Contrastive vision-language models (VLMs), like CLIP, have gained popularity for their versatile applicability to various downstream tasks. Despite their successes in some tasks, l…
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy
Simon Ging, María A. Bravo, Thomas Brox
The evaluation of text-generative vision-language models is a challenging yet crucial endeavor. By addressing the limitations of existing Visual Question Answering (VQA) benchmarks…
Amodal Optical Flow
Maximilian Luz, Rohit Mohan, Ahmed Rida Sekkat +4
Optical flow estimation is very challenging in situations with transparent or occluded objects. In this work, we address these challenges at the task level by introducing Amodal Op…
Climate-sensitive Urban Planning through Optimization of Tree Placements
Simon Schrodi, Ferdinand Briegel, Max Argus +2
Climate change is increasing the intensity and frequency of many extreme weather events, including heatwaves, which results in increased thermal discomfort and mortality rates. Whi…
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
Silvio Galesso, Max Argus, Thomas Brox
The key to out-of-distribution detection is density estimation of the in-distribution data or of its feature representations. This is particularly challenging for dense anomaly det…