Low-shot Object Learning with Mutual Exclusivity Bias
It addresses a challenging low-shot learning task for the ML community, providing a novel dataset and baselines, but is incremental as it builds on existing concepts.
This paper tackles the problem of associating unknown objects in images with category labels using mutual exclusivity bias, a phenomenon from infant word learning, and presents a method that outperforms state-of-the-art models in low-shot accuracy.
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.