论文标题
多级二阶学习
Multi-level Second-order Few-shot Learning
论文作者
论文摘要
我们提出了一个多层次二阶（MLSO），几乎没有弹出的学习网络，用于监督或无监督的几个图像分类和几乎没有射击的动作识别。我们利用所谓的功率差异化二阶基础学习者流，结合表达多个视觉抽象的特征，我们使用自我监督的区分机制。由于二阶合并（SOP）在图像识别中很受欢迎，因此我们在管道中采用了其基本元素的变体。多层次特征设计的目的是在不同层的CNN级别提取特征表示，以实现几个级别的视觉抽象，以实现强大的几次学习。由于SOP可以处理不同空间尺寸的卷积特征图，因此我们还将多个空间尺度的图像输入引入MLSO中。为了利用多层次和多尺度功能的判别信息，我们开发了一个功能匹配（FM）模块，该模块可以重新持续其各自的分支。我们还引入了一个自我监督的步骤，该步骤是空间水平和抽象规模的歧视者。我们的管道以端到端的方式进行了培训。借助简单的体系结构，我们在标准数据集上展示了可观的结果，例如Omniglot，Mini-Imagenet，Siered-Imagenet，开放式麦克风，细粒度的数据集，例如Cub Birds，Stanford Dogs和Stanford Dogs和Cars，以及动作识别数据集，例如HMDB51，UCF101，UCF101和MINI-MIT。
We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or unsupervised few-shot image classification and few-shot action recognition. We leverage so-called power-normalized second-order base learner streams combined with features that express multiple levels of visual abstraction, and we use self-supervised discriminating mechanisms. As Second-order Pooling (SoP) is popular in image recognition, we employ its basic element-wise variant in our pipeline. The goal of multi-level feature design is to extract feature representations at different layer-wise levels of CNN, realizing several levels of visual abstraction to achieve robust few-shot learning. As SoP can handle convolutional feature maps of varying spatial sizes, we also introduce image inputs at multiple spatial scales into MlSo. To exploit the discriminative information from multi-level and multi-scale features, we develop a Feature Matching (FM) module that reweights their respective branches. We also introduce a self-supervised step, which is a discriminator of the spatial level and the scale of abstraction. Our pipeline is trained in an end-to-end manner. With a simple architecture, we demonstrate respectable results on standard datasets such as Omniglot, mini-ImageNet, tiered-ImageNet, Open MIC, fine-grained datasets such as CUB Birds, Stanford Dogs and Cars, and action recognition datasets such as HMDB51, UCF101, and mini-MIT.
