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cs.CV2025

Multi-Object Tracking Retrieval with LLaVA-Video: A Training-Free Solution to MOT25-StAG Challenge

Yi Yang, Yiming Xu, Timo Kaiser +3

In this report, we present our solution to the MOT25-Spatiotemporal Action Grounding (MOT25-StAG) Challenge. The aim of this challenge is to accurately localize and track multiple…

cs.CV2025

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model

Timo Kaiser, Thomas Norrenbrock, Bodo Rosenhahn

The introduction of the Segment Anything Model (SAM) has paved the way for numerous semantic segmentation applications. For several tasks, quantifying the uncertainty of SAM is of…

cs.CV2025

Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty

Timo Kaiser, Maximilian Schier, Bodo Rosenhahn

Cell tracking and segmentation assist biologists in extracting insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches o…

cs.CV2025

QPM: Discrete Optimization for Globally Interpretable Image Classification

Thomas Norrenbrock, Timo Kaiser, Sovan Biswas +2

Understanding the classifications of deep neural networks, e.g. used in safety-critical situations, is becoming increasingly important. While recent models can locally explain a si…

cs.CV2024

CHOTA: A Higher Order Accuracy Metric for Cell Tracking

Timo Kaiser, Vladimir Ulman, Bodo Rosenhahn

The evaluation of cell tracking results steers the development of tracking methods, significantly impacting biomedical research. This is quantitatively achieved by means of evaluat…