论文标题
基于注意力的实例实例歧视性学习重新识别
Unsupervised Attention Based Instance Discriminative Learning for Person Re-Identification
论文作者
论文摘要
重新识别的人的最新进展表明，辨别力增强了，尤其是在有监督的学习或转移学习的情况下。但是，由于数据需求（包括数据策划程度）正在变得越来越复杂和繁琐，因此对无监督的方法至关重要，这些方法对大型内部内部变化具有很强的态度，例如透视，照明，明确的运动，解决方案，解决方案等。因此，我们提出了一个未经经验的人，而没有任何人认可的框架。我们提出的框架利用了一种新的注意机制，该机制结合了组的卷积（1）在多个尺度上增强空间注意力，（2）将可训练参数的数量减少59.6％。此外，我们的框架通过集聚聚类和实例学习以学习解决硬样品的框架共同优化了网络。我们使用Market1501和Dukemtmc-Reid数据集进行了广泛的分析，以证明我们的方法始终优于最先进的方法（具有和没有预训练的权重）。
Recent advances in person re-identification have demonstrated enhanced discriminability, especially with supervised learning or transfer learning. However, since the data requirements---including the degree of data curations---are becoming increasingly complex and laborious, there is a critical need for unsupervised methods that are robust to large intra-class variations, such as changes in perspective, illumination, articulated motion, resolution, etc. Therefore, we propose an unsupervised framework for person re-identification which is trained in an end-to-end manner without any pre-training. Our proposed framework leverages a new attention mechanism that combines group convolutions to (1) enhance spatial attention at multiple scales and (2) reduce the number of trainable parameters by 59.6%. Additionally, our framework jointly optimizes the network with agglomerative clustering and instance learning to tackle hard samples. We perform extensive analysis using the Market1501 and DukeMTMC-reID datasets to demonstrate that our method consistently outperforms the state-of-the-art methods (with and without pre-trained weights).
