Humans can naturally understand an image in depth with the aid of rich
knowledge accumulated from daily lives or professions. For example, to achieve
fine-grained image recognition (e.g., categorizing hundreds of subordinate
categories of birds) usually requires a comprehensive visual concept
organization including category labels and part-level attributes. In this work,
we investigate how to unify rich professional knowledge with deep neural
network architectures and propose a Knowledge-Embedded Representation Learning
(KERL) framework for handling the problem of fine-grained image recognition.
Specifically, we organize the rich visual concepts in the form of knowledge
graph and employ a Gated Graph Neural Network to propagate node message through
the graph for generating the knowledge representation. By introducing a novel
gated mechanism, our KERL framework incorporates this knowledge representation
into the discriminative image feature learning, i.e., implicitly associating
the specific attributes with the feature maps. Compared with existing methods
of fine-grained image classification, our KERL framework has several appealing
properties: i) The embedded high-level knowledge enhances the feature
representation, thus facilitating distinguishing the subtle differences among
subordinate categories. ii) Our framework can learn feature maps with a
meaningful configuration that the highlighted regions finely accord with the
nodes (specific attributes) of the knowledge graph. Extensive experiments on
the widely used Caltech-UCSD bird dataset demonstrate the superiority of our
KERL framework over existing state-of-the-art methods.

本文提出了一个知识图谱和门控图神经网络相结合的框架，名为知识嵌入表示学习框架，用于处理细粒度图像识别问题，并在广泛使用的 Caltech-UCSD 鸟数据集上进行了大量实验，证明了该框架超越现有的最先进方法。

基于知识嵌入的细粒度图像识别表征学习

Knowledge-Embedded Representation Learning for Fine-Grained Image  Recognition

Fine-grained categorisation has been a challenging problem due to small
inter-class variation, large intra-class variation and low number of training
images. We propose a learning system which first clusters visually similar
classes and then learns deep convolutional neural network features specific to
each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset
show that the proposed method outperforms recent fine-grained categorisation
methods under the most difficult setting: no bounding boxes are presented at
test time. It achieves a mean accuracy of 77.5%, compared to the previous best
performance of 73.2%. We also show that progressive transfer learning allows us
to first learn domain-generic features (for bird classification) which can then
be adapted to specific set of bird classes, yielding improvements in accuracy.

本文提出了一种基于深度卷积神经网络的学习系统，该系统对视觉相似的类别进行聚类，并学习针对每个子集的特定深度卷积神经网络特征，在测试时不需要边界框。在流行的 Caltech-UCSD 鸟类数据集上，实验证明该方法优于最近的细粒度分类方法，无需边界框，在最困难的情况下达到了 77.5% 的平均准确率，而先前的最佳表现为 73.2%。本文还表明，渐进式迁移学习使我们能够先学习通用领域的特征，然后将其调整到特定的鸟类集合中，从而提高准确性。