6 papers · 1 filter
Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision
Mateo Diaz-Bone, Daniel Caraballo, Florian Scheidegger +11
Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec. However, many of these methods face challenges when…
Cracks in the Foundation: A Civil Infrastructure Dataset to Challenge Vision Foundation Models
Nicola Farronato, Niccolo Avogaro, Thomas Frick +6
Automated structural health monitoring is essential to prevent catastrophic infrastructure failures. Precise, pixel-level defect segmentation is needed to accurately assess structu…
Enhancing Semantic Segmentation with Continual Self-Supervised Pre-training
Brown Ebouky, Ajad Chhatkuli, Cristiano Malossi +3
Self-supervised learning (SSL) has emerged as a central paradigm for training foundation models by leveraging large-scale unlabeled datasets, often producing representations with s…
Q-SAM2: Accurate Quantization for Segment Anything Model 2
Nicola Farronato, Florian Scheidegger, Mattia Rigotti +3
The Segment Anything Model 2 (SAM2) is a powerful foundation model for promptable segmentation. However, its high computational and memory costs are a major barrier to deployment o…
VP Lab: a PEFT-Enabled Visual Prompting Laboratory for Semantic Segmentation
Niccolo Avogaro, Thomas Frick, Yagmur G. Cinar +12
Large-scale pretrained vision backbones have transformed computer vision by providing powerful feature extractors that enable various downstream tasks, including training-free appr…
Show or Tell? Effectively prompting Vision-Language Models for semantic segmentation
Niccolo Avogaro, Thomas Frick, Mattia Rigotti +5
Large Vision-Language Models (VLMs) are increasingly being regarded as foundation models that can be instructed to solve diverse tasks by prompting, without task-specific training.…