论文标题
几次衡量指标：在线改编嵌入以进行检索
Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval
论文作者
论文摘要
公制学习旨在通过学习有效的嵌入功能来建立一个距离度量，该函数将相似的对象映射到其嵌入空间附近点。尽管深度度量学习最近取得了进步，但学到的指标将概括为具有巨大领域差距的阶级概括仍然具有挑战性。为了解决这个问题，我们探索了一个新的问题的新问题，该问题旨在仅使用几个带注释的数据将嵌入功能调整到目标域。我们介绍了三个少量的度量学习基线，并提出了通道直线元素元学习（CRML），该基线通过调整中间层的通道来有效地在线适应度量空间。对Miniimagenet，Cub-200-2011，MPII以及新数据集进行的实验分析，即MinideEpfashion，表明，当从源类中较大的域间隙时，我们的方法始终通过将其调整为目标类别并实现图像检索而获得更大的图像检索来改善该指标。
Metric learning aims to build a distance metric typically by learning an effective embedding function that maps similar objects into nearby points in its embedding space. Despite recent advances in deep metric learning, it remains challenging for the learned metric to generalize to unseen classes with a substantial domain gap. To tackle the issue, we explore a new problem of few-shot metric learning that aims to adapt the embedding function to the target domain with only a few annotated data. We introduce three few-shot metric learning baselines and propose the Channel-Rectifier Meta-Learning (CRML), which effectively adapts the metric space online by adjusting channels of intermediate layers. Experimental analyses on miniImageNet, CUB-200-2011, MPII, as well as a new dataset, miniDeepFashion, demonstrate that our method consistently improves the learned metric by adapting it to target classes and achieves a greater gain in image retrieval when the domain gap from the source classes is larger.
