May 18, 2021 · We propose the Cross Non-Local Neural Network (CNL) for capturing the long-range dependency of the samples and the current task.
Abstract. Building a good feature space is essential for the metric-based few-shot algorithms to recognize a novel class with only a few samples.
The Cross Non-Local Neural Network (CNL) is proposed for capturing the long-range dependency of the samples and the current task, and extracts the ...
Building a good feature space is essential for the metric-based few-shot algorithms to recognize a novel class with only a few samples.
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A good feature representation learning method promotes to further improve the performance of few-shot learning under limited samples[8]. designs the feature ...
May 31, 2023 · [AAAI 2021] (paper) Looking Wider for Better Adaptive Representation in Few-Shot Learning. 67.96 / 83.36 for mini-ImageNet; 73.42 / 87.72 for ...
Sep 26, 2024 · We propose a novel saliency-based end-to-end meta-hallucinator, where a saliency detector produces foregrounds and backgrounds of support images.
Oct 5, 2024 · Yang, X. Lin, J. Yang, and L. He, “Looking wider for better adaptive representation in few-shot learning,” Proceedings of the AAAI Conference ...
Sep 7, 2022 · Many few-shot visual recognition methods adopt the metric-based meta-learning paradigm by comparing the query representation with class.
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Sep 26, 2024 · Zhao J, Yang Y, Lin X, et al. Looking wider for better adaptive representation in few-shot learning. In: Proceedings of the AAAI Conference on ...