269 citations · 550 across the 34 of their papers we have counts for
3 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,…
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,…
PANDAS: Prototype-based Novel Class Discovery and Detection
Tyler L. Hayes, César R. de Souza, Namil Kim +3
Object detectors are typically trained once and for all on a fixed set of classes. However, this closed-world assumption is unrealistic in practice, as new classes will inevitably…