论文标题
顶部DB-NET：重新识别激活增强的顶部下降块
Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification
论文作者
论文摘要
人重新识别是一项具有挑战性的任务，旨在在非重叠摄像机系统上检索所有查询图像的实例。由于观点的各种极端变化，很常见的是，可以用来匹配人的地方区域被抑制，这导致了一种场景，在这种情况下，方法必须根据信息较少的区域评估图像的相似性。在这项工作中，我们介绍了Top-DB-NET，这是一种基于顶级底座的方法，它推动网络学习专注于场景前景，特别强调与任务最相关的区域，同时，编码低信息的区域以提供高可辨别性。前DB-NET由三个流组成：（i）全局流从骨干线中编码丰富的图像信息，（ii）顶部的Dropblock流鼓励骨干来编码具有高歧视特征的低信息范围的低信息区域，并且（iii）正则化流有助于处理第二个流的噪声，在测试第一个流中的第二个流噪声中，可以处理第二个流的噪声。在三个具有挑战性的数据集上进行的大量实验表明了我们针对最先进方法的方法的能力。定性结果表明，我们的方法表现出更好的激活图，重点是输入图像的可靠部分。
Person Re-Identification is a challenging task that aims to retrieve all instances of a query image across a system of non-overlapping cameras. Due to the various extreme changes of view, it is common that local regions that could be used to match people are suppressed, which leads to a scenario where approaches have to evaluate the similarity of images based on less informative regions. In this work, we introduce the Top-DB-Net, a method based on Top DropBlock that pushes the network to learn to focus on the scene foreground, with special emphasis on the most task-relevant regions and, at the same time, encodes low informative regions to provide high discriminability. The Top-DB-Net is composed of three streams: (i) a global stream encodes rich image information from a backbone, (ii) the Top DropBlock stream encourages the backbone to encode low informative regions with high discriminative features, and (iii) a regularization stream helps to deal with the noise created by the dropping process of the second stream, when testing the first two streams are used. Vast experiments on three challenging datasets show the capabilities of our approach against state-of-the-art methods. Qualitative results demonstrate that our method exhibits better activation maps focusing on reliable parts of the input images.
