3 papers
cs.CV2025
Stochastic-based Patch Filtering for Few-Shot Learning
Javier Rodenas, Eduardo Aguilar, Petia Radeva
Food images present unique challenges for few-shot learning models due to their visual complexity and variability. For instance, a pasta dish might appear with various garnishes on…
cs.CV2025
Slot Attention-based Feature Filtering for Few-Shot Learning
Javier Rodenas, Eduardo Aguilar, Petia Radeva
Irrelevant features can significantly degrade few-shot learn ing performance. This problem is used to match queries and support images based on meaningful similarities despite the…
cs.CV2025
Multi-label out-of-distribution detection via evidential learning
Eduardo Aguilar, Bogdan Raducanu, Petia Radeva
A crucial requirement for machine learning algorithms is not only to perform well, but also to show robustness and adaptability when encountering novel scenarios. One way to achiev…