Wandering Within a World: Online Contextualized Few-Shot Learning

By Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel. 2020

33 citations recorded by the source corpus.

We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this setting, episodes do not have separate training and testing phases, and instead models are evaluated online while learning novel classes. As in the real world, where the presence of spatiotemporal context helps us retrieve learned skills in the past, our online few-shot learning setting also features an underlying context that changes throughout time. Object classes are correlated within a context and inferring the correct context can lead to better performance. Building upon this setting, we propose a new few-shot learning dataset based on large scale indoor imagery that mimics the visual experience of an agent wandering within a world. Furthermore, we convert popular few-shot learning approaches into online versions and we also propose a new contextual prototypical memory model that can make use of spatiotemporal contextual information from the recent past.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Recent Advances of Continual Learning in Computer Vision: An Overview (2021, neutral): “[114] introduced a new problem setup called Online Contextualized Few-Shot Learning (OC-FSL).”
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer (2021, neutral): “We acknowledge other concurrent works push realistic continual learning settings as well (Mundt et al., 2020; Ren et al., 2020), but the contributions are orthogonal to our setting.”
Variable-Shot Adaptation for Online Meta-Learning (2020, methodology): “Unlike prior online meta-learning works (Finn et al., 2019; Grant et al., 2019; Khodak et al., 2019; Zhuang et al., 2019; Ren et al., 2020; Antoniou et al., 2020; Wallingford et al., 2020; Yao et al., 2020), we develop an algorithm that is specifically tailored to leverage variable amounts of data, ”
GDumb: A Simple Approach that Questions Our Progress in Continual Learning (2020, background): “This formulation is unrealistic in the sense that it does not allow dynamic contexts [40].”
Learning to Learn and Remember Super Long Multi-Domain Task Sequence (2022, methodology): “Continual meta-learning [1, 13, 54, 74] is to apply the meta-learning techniques for continual learning.”