论文标题
重新识别的分层双向特征感知网络
Hierarchical Bi-Directional Feature Perception Network for Person Re-Identification
论文作者
论文摘要
上人重新识别（RE-ID）模型旨在专注于图像的最歧视区域，而当相机观点变化或遮挡引起该区域时，其性能可能会损害。为了解决此问题，我们提出了一个名为层次双向特征感知网络（HBFP-NET）的新型模型，以关联多级信息并相互加强。首先，跨级特征对的相关图是通过低级双线性池建模的。然后，基于相关图，使用双向特征感知（BFP）模块来丰富高级特征的注意区域，并在低级特征中学习抽象和特定信息。然后，我们提出了一个新颖的端到端分层网络，该网络集成了多级增强功能，并输入增强的低和中层特征以以下图层以重新培训新的功能强大的网络。更重要的是，我们提出了一种新型的可训练的广义合并，该合并可以动态选择要激活的特征图中所有位置的任何值。在包括Market-1501，Cuhk03和Dukemtmc-Reid在内的主流评估数据集上实施的广泛实验表明，我们的方法的表现优于最近的SOTA RE-ID模型。
Previous Person Re-Identification (Re-ID) models aim to focus on the most discriminative region of an image, while its performance may be compromised when that region is missing caused by camera viewpoint changes or occlusion. To solve this issue, we propose a novel model named Hierarchical Bi-directional Feature Perception Network (HBFP-Net) to correlate multi-level information and reinforce each other. First, the correlation maps of cross-level feature-pairs are modeled via low-rank bilinear pooling. Then, based on the correlation maps, Bi-directional Feature Perception (BFP) module is employed to enrich the attention regions of high-level feature, and to learn abstract and specific information in low-level feature. And then, we propose a novel end-to-end hierarchical network which integrates multi-level augmented features and inputs the augmented low- and middle-level features to following layers to retrain a new powerful network. What's more, we propose a novel trainable generalized pooling, which can dynamically select any value of all locations in feature maps to be activated. Extensive experiments implemented on the mainstream evaluation datasets including Market-1501, CUHK03 and DukeMTMC-ReID show that our method outperforms the recent SOTA Re-ID models.
