节点文献
用于行人重识别的多类型特征网络
Multi-type Features Network for Person Re-identification
【摘要】 近年来,注意机制在行人重识别任务中效果较优,但是不同类型的注意机制(如空间注意、自注意等)联合使用的效果仍然有待提高.因此,文中首先提出改进型的卷积块注意模型(CBAM-Pro),再提出多类型特征网络模型.对CBAM-Pro与自注意机制的集成提取不同关注域的特征,同时引入不同划分粒度的局部特征,联合进行行人重识别.在现有的通用基准数据集上的实验验证文中模型的有效性与可靠性.
【Abstract】 The attention mechanism is effective in person re-identification. However, the performance of the combined use of different types of attention mechanisms needs to be improved, such as spatial attention and self-attention. An improved convolutional block attention model(CBAM-PRO) is proposed, and then a multi-type features network(MTFN) is proposed. The features of different interested domains are extracted through the integration of CBAM-Pro and self-attention mechanism, and the local features with different granularities are introduced concurrently to perform person re-identification jointly. The validity and reliability of MTFN are verified by the experiments on the existing general benchmark datasets.
【Key words】 Person Re-identification; Attention Mechanism; Feature Partition; Multi-type Features;
- 【文献出处】 模式识别与人工智能 ,Pattern Recognition and Artificial Intelligence , 编辑部邮箱 ,2020年10期
- 【分类号】TP391.41;TP183
- 【被引频次】4
- 【下载频次】228