论文标题
重新访问姿势归一化，以进行细颗粒的几分识别
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
论文作者
论文摘要
很少有细粒度的分类需要一个模型来学习基于一些图像的不同类别（例如鸟类）之间微妙的细粒度区分。 This requires a remarkable degree of invariance to pose, articulation and background.一种解决方案是使用姿势归一化表示：首先在每个图像中定位语义部分，然后通过表征每个部分的外观来描述图像。尽管此类表示对完全有监督的分类不满意，但我们表明它们对于几种细颗粒分类非常有效。随着模型容量的最小增加，姿势归一化可提高浅层和深度体系结构的10到20个百分点的精度，从而更好地概括为新领域，并且对多个几次弹出算法和网络骨架有效。 Code is available at https://github.com/Tsingularity/PoseNorm_Fewshot
Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e.g., birds) based on a few images alone. This requires a remarkable degree of invariance to pose, articulation and background. A solution is to use pose-normalized representations: first localize semantic parts in each image, and then describe images by characterizing the appearance of each part. While such representations are out of favor for fully supervised classification, we show that they are extremely effective for few-shot fine-grained classification. With a minimal increase in model capacity, pose normalization improves accuracy between 10 and 20 percentage points for shallow and deep architectures, generalizes better to new domains, and is effective for multiple few-shot algorithms and network backbones. Code is available at https://github.com/Tsingularity/PoseNorm_Fewshot
