356 citations · 819 across the 28 of their papers we have counts for
4 papers · 1 filter
What could go wrong? Discovering and describing failure modes in computer vision
Gabriela Csurka, Tyler L. Hayes, Diane Larlus +1
Deep learning models are effective, yet brittle. Even carefully trained, their behavior tends to be hard to predict when confronted with out-of-distribution samples. In this work,…
Self-supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement
Maxime Pietrantoni, Gabriela Csurka, Martin Humenberger +1
Visual localization techniques rely upon some underlying scene representation to localize against. These representations can be explicit such as 3D SFM map or implicit, such as a n…
SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection
Mingxuan Liu, Tyler L. Hayes, Elisa Ricci +2
Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inferen…
Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency
Yannis Kalantidis, Mert Bülent Sarıyıldız, Rafael S. Rezende +3
State-of-the-art visual localization approaches generally rely on a first image retrieval step whose role is crucial. Yet, retrieval often struggles when facing varying conditions,…