论文标题
频道重要性在几个图像分类中很重要
Channel Importance Matters in Few-Shot Image Classification
论文作者
论文摘要
很少有射击学习（FSL）需要视觉模型来快速适应任务分布的变化的全新分类任务。了解此任务分配转移带来的困难是FSL的核心。在本文中，我们表明，从频道的角度来看，简单的频道特征转换可能是揭开此秘密的关键。当在测试时间数据集中面对新颖的少量任务时，这种转换可以极大地提高学到的图像表示的概括能力，同时对培训算法和数据集的选择不可知。通过对这种转变的深入分析，我们发现FSL中表示的难度源于图像表示的严重渠道偏置问题：渠道在不同任务中的重要性可能不同，而卷积神经网络可能不敏感，或者对这种转变的反应不正确。这指出了现代视觉系统的概括能力和未来需要进一步关注的核心问题。我们的代码可在https://github.com/frankluox/channel_importance_fsl上找到。
Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new classification tasks with a shift in task distribution. Understanding the difficulties posed by this task distribution shift is central to FSL. In this paper, we show that a simple channel-wise feature transformation may be the key to unraveling this secret from a channel perspective. When facing novel few-shot tasks in the test-time datasets, this transformation can greatly improve the generalization ability of learned image representations, while being agnostic to the choice of training algorithms and datasets. Through an in-depth analysis of this transformation, we find that the difficulty of representation transfer in FSL stems from the severe channel bias problem of image representations: channels may have different importance in different tasks, while convolutional neural networks are likely to be insensitive, or respond incorrectly to such a shift. This points out a core problem of the generalization ability of modern vision systems and needs further attention in the future. Our code is available at https://github.com/Frankluox/Channel_Importance_FSL.
