6 citations · 13 across the 23 of their papers we have counts for
9 papers · 1 filter
SciFusion: Physics-Guided Compositional Diffusion for Multimodal 3D Scientific Image Generation
Luoxi Wang, Alessio Quercia, Hanno Scharr +1
Existing 3D generative approaches are typically developed for individual imaging modalities, requiring substantial modality-specific data and limiting their ability to exploit shar…
AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture
Shengjie Guo, Xin Li, Borjana Arsova +2
Accurate counting and localization of plants and their organs support phenotyping and yield estimation, yet target appearance, scale, and density vary widely across species and ima…
Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion
Christian Heinemann, Freddie Åström, George Baravdish +3
In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel…
RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data
Xuan Zhao, Lena Krieger, Zhuo Cao +3
Counterfactual explanations (CFs) help understand machine learning models by identifying minimal input changes that would lead to alternative model outcomes. Recent work demonstrat…
Residual-Space Evolutionary Optimization via Flow-based Generative Models
Zhuo Cao, Lena Krieger, Fernanda Nader +3
Data editing with generative methods typically requires differentiable objectives and gradient-based search. However, these assumptions break down in flow-based settings, where edi…
DART: A design-aware microfluidic chip paradigm for real-time live-cell image analysis
Johannes Seiffarth, Matthias Pesch, Lukas Scholtes +3
High-throughput microfluidic live-cell imaging generates rich single-cell data. Yet semi-automated procedures for locating regions of interest (RoIs), each containing one cell popu…
Counterfactual Transport Flows for Offline Conservative Trajectory Refinement
Lena Krieger, Xuan Zhao, Zhuo Cao +3
Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback. A key diffi…
Higher-Order Hit-&-Run Samplers for Linearly Constrained Densities
Richard D. Paul, Anton Stratmann, Johann F. Jadebeck +4
Markov chain Monte Carlo (MCMC) sampling of densities restricted to linearly constrained domains is an important task arising in Bayesian treatment of inverse problems in the natur…
Least but not Last: Fine-tuning Intermediate Principal Components for Better Performance-Forgetting Trade-Offs
Alessio Quercia, Arya Bangun, Ira Assent +1
Low-Rank Adaptation (LoRA) methods have emerged as crucial techniques for adapting large pre-trained models to downstream tasks under computational and memory constraints. However,…