论文标题
不完整的描述师挖掘和人重新识别的弹性损失
Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification
论文作者
论文摘要
在本文中，我们提出了一个新颖的人重新建立模型，即连续的批处理底座网络（CBDB-net），以捕获人重新任务的细心和强大的人描述符。 CBDB-NET包含两个新型设计：连续的批处理模块（CBDBM）和弹性损耗（EL）。在连续的批处理模块（CBDBM）中，我们首先在特征图上进行统一分区。然后，我们独立且连续地将每个贴片从特征图上的顶部到底部删除，这可以输出多个不完整的特征映射。在训练阶段，这些多个不完整的功能可以更好地鼓励重新ID模型捕获重新ID任务的强大人物描述符。在弹性损失（EL）中，我们设计了一个新型的重量控制项目，以帮助重新ID模型在整个训练过程中适应性平衡硬样品对和易于样本对。通过大量的消融研究，我们验证了连续的批处理底座模块（CBDBM）和弹性损失（EL）每个都会有助于CBDB-NET的性能提升。我们证明，我们的CBDB-NET可以在三个标准人员重新ID数据集（Market-1501，Dukemtmc-Re-ID和CuHK03数据集）上实现竞争性能，这是三个闭塞的人重新数据集（Re-ID数据集） （车内服装检索数据集）。
In this paper, we propose a novel person Re-ID model, Consecutive Batch DropBlock Network (CBDB-Net), to capture the attentive and robust person descriptor for the person Re-ID task. The CBDB-Net contains two novel designs: the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL). In the Consecutive Batch DropBlock Module (CBDBM), we firstly conduct uniform partition on the feature maps. And then, we independently and continuously drop each patch from top to bottom on the feature maps, which can output multiple incomplete feature maps. In the training stage, these multiple incomplete features can better encourage the Re-ID model to capture the robust person descriptor for the Re-ID task. In the Elastic Loss (EL), we design a novel weight control item to help the Re-ID model adaptively balance hard sample pairs and easy sample pairs in the whole training process. Through an extensive set of ablation studies, we verify that the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL) each contribute to the performance boosts of CBDB-Net. We demonstrate that our CBDB-Net can achieve the competitive performance on the three standard person Re-ID datasets (the Market-1501, the DukeMTMC-Re-ID, and the CUHK03 dataset), three occluded Person Re-ID datasets (the Occluded DukeMTMC, the Partial-REID, and the Partial iLIDS dataset), and a general image retrieval dataset (In-Shop Clothes Retrieval dataset).
