Attribute recognition has become crucial because of its wide applications in
many computer vision tasks, such as person re-identification. Like many object
recognition problems, variations in viewpoints, illumination, and recognition
at far distance, all make this task challenging. In this work, we propose a
joint CTC-Attention model (JCM), which maps attribute labels into sequences to
learn the semantic relationship among attributes. Besides, this network uses
neural network to encode images into sequences, and employs connectionist
temporal classification (CTC) loss to train the network with the aim of
improving the encoding performance of the network. At the same time, it adopts
the attention model to decode the sequences, which can realize aligning the
sequences and better learning the semantic information from attributes.
Extensive experiments on three public datasets, i.e., Market-1501 attribute
dataset, Duke attribute dataset and PETA dataset, demonstrate the effectiveness
of the proposed method.

本文提出了一种联合 CTC-Attention 模型（JCM），该模型将属性标签映射到序列中以学习属性之间的语义关系，并采用神经网络将图像编码为序列，采用了注意力模型解码序列，从而更好地学习语义信息，实验结果表明该方法对属性识别具有有效性。