34 citations · 113 across the 36 of their papers we have counts for
13 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…
GazeVLM: Active Vision via Internal Attention Control for Multimodal Reasoning
Brown Ebouky, Gabriele Carrino, Niccolo Avogaro +3
Human visual reasoning is governed by active vision, a process where meta-cognitive control drives top-down goal-directed attention, dynamically routing foveal focus toward task-re…
SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs
Niccolo Avogaro, Nayanika Debnath, Li Mi +6
Despite recent successes, test-time scaling -- i.e., dynamically expanding the token budget during inference as needed -- remains brittle for vision-language models (VLMs). Unstruc…
Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Model for Microclimate Impact Prediction
Jannis Fleckenstein, David Kreismann, Tamara Rosemary Govindasamy +3
As urbanization and climate change progress, urban heat island effects are becoming more frequent and severe. To formulate effective mitigation plans, cities require detailed air t…
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…