Author name cluster

Riquan Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2020 Conference Paper

Knowledge Graph Transfer Network for Few-Shot Recognition

Riquan Chen
Tianshui Chen
Xiaolu Hui
Hefeng Wu
Guanbin Li
Liang Lin

Few-shot learning aims to learn novel categories from very few samples given some base categories with sufﬁcient training samples. The main challenge of this task is the novel categories are prone to dominated by color, texture, shape of the object or background context (namely speciﬁcity), which are distinct for the given few training samples but not common for the corresponding categories (see Figure 1). Fortunately, we ﬁnd that transferring information of the correlated based categories can help learn the novel concepts and thus avoid the novel concept being dominated by the speciﬁcity. Besides, incorporating semantic correlations among different categories can effectively regularize this information transfer. In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN). Specifically, by initializing each node with the classiﬁer weight of the corresponding category, a propagation mechanism is learned to adaptively propagate node message through the graph to explore node interaction and transfer classiﬁer information of the base categories to those of the novel ones. Extensive experiments on the ImageNet dataset show significant performance improvement compared with current leading competitors. Furthermore, we construct an ImageNet-6K dataset that covers larger scale categories, i. e, 6, 000 categories, and experiments on this dataset further demonstrate the effectiveness of our proposed model.

PDF Details

IJCAI Conference 2018 Conference Paper

Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition

Tianshui Chen
Liang Lin
Riquan Chen
Yang Wu
Xiaonan Luo

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.

PDF Details