8 citations · 10 across the 8 of their papers we have counts for
9 papers
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…
CHiQPM: Calibrated Hierarchical Interpretable Image Classification
Thomas Norrenbrock, Timo Kaiser, Sovan Biswas +3
Globally interpretable models are a promising approach for trustworthy AI in safety-critical domains. Alongside global explanations, detailed local explanations are a crucial compl…
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…
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…
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…
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…
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization
Patrick Glandorf, Timo Kaiser, Bodo Rosenhahn
Sparse neural networks are a key factor in developing resource-efficient machine learning applications. We propose the novel and powerful sparse learning method Adaptive Regularize…
Compensation Learning in Semantic Segmentation
Timo Kaiser, Christoph Reinders, Bodo Rosenhahn
Label noise and ambiguities between similar classes are challenging problems in developing new models and annotating new data for semantic segmentation. In this paper, we propose C…
Blind Knowledge Distillation for Robust Image Classification
Timo Kaiser, Lukas Ehmann, Christoph Reinders +1
Optimizing neural networks with noisy labels is a challenging task, especially if the label set contains real-world noise. Networks tend to generalize to reasonable patterns in the…