5 papers
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…
ClustViT: Clustering-based Token Merging for Semantic Segmentation
Fabio Montello, Ronja Güldenring, Lazaros Nalpantidis
Vision Transformers can achieve high accuracy and strong generalization across various contexts, but their practical applicability on real-world robotic systems is limited due to t…
Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
Patrick Schmidt, Vasileios Belagiannis, Lazaros Nalpantidis
Autonomous robotic systems applied to new domains require an abundance of expensive, pixel-level dense labels to train robust semantic segmentation models under full supervision. T…
A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion
Fabio Montello, Ronja Güldenring, Simone Scardapane +1
Model compression is essential in the deployment of large Computer Vision models on embedded devices. However, static optimization techniques (e.g. pruning, quantization, etc.) neg…
From Web Data to Real Fields: Low-Cost Unsupervised Domain Adaptation for Agricultural Robots
Vasileios Tzouras, Lazaros Nalpantidis, Ronja Güldenring
In precision agriculture, vision models often struggle with new, unseen fields where crops and weeds have been influenced by external factors, resulting in compositions and appeara…