13 citations · 20 across the 7 of their papers we have counts for
7 papers
CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?
Jonathan Sadeghi, Jenny Seidenschwarz, Jesse Allardice +3
Video world models approximate the stochastic distribution of physical outcomes through generative sampling, but existing benchmarks score individual generations or compare distrib…
PROWL: Prioritized Regret-Driven Optimization for World Model Learning
Ahmet H. Güzel, Jenny Seidenschwarz, Benjamin Graham +3
Modern action-conditioned video world models achieve strong short-horizon visual realism, yet remain unreliable on rare, interaction-critical transitions that dominate downstream p…
DynOMo: Online Point Tracking by Dynamic Online Monocular Gaussian Reconstruction
Jenny Seidenschwarz, Qunjie Zhou, Bardienus Duisterhof +2
Reconstructing scenes and tracking motion are two sides of the same coin. Tracking points allow for geometric reconstruction [14], while geometric reconstruction of (dynamic) scene…
SeMoLi: What Moves Together Belongs Together
Jenny Seidenschwarz, Aljoša Ošep, Francesco Ferroni +2
We tackle semi-supervised object detection based on motion cues. Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to p…
Simple Cues Lead to a Strong Multi-Object Tracker
Jenny Seidenschwarz, Guillem Brasó, Victor Castro Serrano +2
For a long time, the most common paradigm in Multi-Object Tracking was tracking-by-detection (TbD), where objects are first detected and then associated over video frames. For asso…
The Group Loss++: A deeper look into group loss for deep metric learning
Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner +4
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings…
Learning Intra-Batch Connections for Deep Metric Learning
Jenny Seidenschwarz, Ismail Elezi, Laura Leal-Taixé
The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric l…