In this paper, we propose an Adaptive Task-aware Local Rep- resentation Network (ATL-Net) for few-shot learning, aiming to learn more discriminative local ...
This paper proposes an Adaptive Task-aware Local Representations Network (ATL-Net) to address this limitation by introducing episodic attention, which can ...
Jan 7, 2021 · This paper proposes an Adaptive Task-aware Local Representations Network (ATL-Net) to address this limitation by introducing episodic attention, ...
[PDF] Learning Task-aware Local Representations for Few-shot ...
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How to transfer information or knowledge? • How to select important information among the task? • How to measure the similarity between the query image and ...
Few-shot learning for visual recognition aims to adapt to novel unseen classes with only a few images. Recent work, especially the work based on low-level ...
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About. The code of "Learning Task-aware Local Representations for Few-shot Learning", IJCAI 2020. Topics. computer-vision few-shot-learning. Resources.
Oct 21, 2023 · Few-shot learning for image classification task aims to classify images from several novel classes with limited number of samples.
The task-aware part filters can adapt to any individual task and automatically mine task-related local parts even for an unseen task. Second, an adaptive ...
摘要. Few-shot learning for visual recognition aims to adapt to novel unseen classes with only a few images. Recent work, e.
This work is based on metric-based few-shot learning methods. Metric learning approaches primarily focus on concept representation or relationship measurement.