LAUSR

Similarity learning is often adopted as an auxiliary task of deep multitask learning methods to learn discriminant features. Most existing approaches only use the single-layer features extracted by the last fully connected layer, which ignores the abundant information of feature channels in lower layers. Besides, small cliques are the most commonly used methods in similarity learning tasks to model the correlation of data, which can lead to the limited relation learning. In this article, we present an end-to-end hierarchical deep multitask learning framework for similarity learning which can learn more discriminant features by sharing information from different layers of network and dealing with complex correlation. Its main task is graph similarity inference. We build focus graphs for each sample. Then, an attention mechanism and a node feature enhancing model are introduced into backbone network to extract the abundant and important channel information from multiple layers of network. In the similarity inference task, a relation enhancing mechanism is applied to graph convolutional network to leverage the crucial relation in channels, which can effectively facilitate the learning ability of the whole framework. The extensive experiments have been conducted to demonstrate the effectiveness of the proposed method on person reidentification and face clustering applications.