4 papers
Feature-Space Generative Models for One-Shot Class-Incremental Learning
Jack Foster, Kirill Paramonov, Mete Ozay +1
Few-shot class-incremental learning (FSCIL) is a paradigm where a model, initially trained on a dataset of base classes, must adapt to an expanding problem space by recognizing nov…
Continual Error Correction on Low-Resource Devices
Kirill Paramonov, Mete Ozay, Aristeidis Mystakidis +11
The proliferation of AI models in everyday devices has highlighted a critical challenge: prediction errors that degrade user experience. While existing solutions focus on error det…
Controllable Forgetting Mechanism for Few-Shot Class-Incremental Learning
Kirill Paramonov, Mete Ozay, Eunju Yang +2
Class-incremental learning in the context of limited personal labeled samples (few-shot) is critical for numerous real-world applications, such as smart home devices. A key challen…
Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search
Kirill Paramonov, Jia-Xing Zhong, Umberto Michieli +2
In this paper, we address a recent trend in robotic home appliances to include vision systems on personal devices, capable of personalizing the appliances on the fly. In particular…