6 papers · 1 filter
AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining
Vasileios Tzouras, Paraskevas Pegios, Lazaros Nalpantidis
Field-based agricultural computer vision is important for precision agriculture, yet it largely depends on expensive annotations and costly adaptation of large pretrained models. W…
Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion
Jakub Gregorek, Paraskevas Pegios, Nando Metzger +3
We introduce Marigold-SSD, a single-step, late-fusion depth completion framework that leverages strong diffusion priors while eliminating the costly test-time optimization typicall…
Weight Space Correlation Analysis: Quantifying Feature Utilization in Deep Learning Models
Chun Kit Wong, Paraskevas Pegios, Nina Weng +4
Deep learning models in medical imaging are susceptible to shortcut learning, relying on confounding metadata (e.g., scanner model) that is often encoded in image embeddings. The c…
Causally Steered Diffusion for Automated Video Counterfactual Generation
Nikos Spyrou, Athanasios Vlontzos, Paraskevas Pegios +5
Adapting text-to-image (T2I) latent diffusion models (LDMs) to video editing has shown strong visual fidelity and controllability, but challenges remain in maintaining causal relat…
Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
Nina Weng, Paraskevas Pegios, Eike Petersen +2
Shortcut learning is when a model -- e.g. a cardiac disease classifier -- exploits correlations between the target label and a spurious shortcut feature, e.g. a pacemaker, to predi…
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
Manxi Lin, Jakob Ambsdorf, Emilie Pi Fogtmann Sejer +9
We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements. Working in fetal ultrasound, where ranking is challenging an…