Low-shot Object Learning with Mutual Exclusivity Bias
Anh Thai Ahmad Humayun Stefan Stojanov Zixuan Huang Bikram Boote James M. Rehg
This paper introduces Low-shot Object Learning with Mutual Exclusivity Bias
(LSME), the first computational framing of mutual exclusivity bias, a
phenomenon commonly observed in infants during word learning. We provide a
novel dataset, comprehensive baselines, and a state-of-the-art method to enable
the ML community to tackle this challenging learning task. The goal of LSME is
to analyze an RGB image of a scene containing multiple objects and correctly
associate a previously-unknown object instance with a provided category label.
This association is then used to perform low-shot learning to test category
generalization. We provide a data generation pipeline for the LSME problem and
conduct a thorough analysis of the factors that contribute to its difficulty.
Additionally, we evaluate the performance of multiple baselines, including
state-of-the-art foundation models. Finally, we present a baseline approach
that outperforms state-of-the-art models in terms of low-shot accuracy.