Abstract
Person retrieval faces many challenges including cluttered background, appearance variations (e.g., illumination, pose, occlusion) among different camera views and the similarity among different person's images. To address these issues, we put forward a novel mask based deep ranking neural network with a skipped fusing layer. Firstly, to alleviate the problem of cluttered background, masked images with only the foreground regions are incorporated as input in the proposed neural network. Secondly, to reduce the impact of the appearance variations, the multi-layer fusion scheme is developed to obtain more discriminative fine-grained information. Lastly, considering person retrieval is a special image retrieval task, we propose a novel ranking loss to optimize the whole network. The proposed ranking loss can further mitigate the interference problem of similar negative samples when producing ranking results. The extensive experiments validate the superiority of the proposed method compared with the state-of-the-art methods on many benchmark datasets.
Abstract (translated)
人的检索面临着许多挑战，包括背景混乱、不同摄像机视图之间的外观变化（如照明、姿势、遮挡）以及不同人图像之间的相似性。为了解决这些问题，我们提出了一种基于掩模的跳过融合层的深度排序神经网络。首先，为了解决背景杂乱的问题，在神经网络中引入了仅包含前景区域的屏蔽图像作为输入。其次，为了减少外观变化的影响，开发了多层融合方案，以获得更具识别性的细粒度信息。最后，考虑到人脸检索是一项特殊的图像检索任务，我们提出了一种新的排名损失优化整个网络。提出的排序损失在产生排序结果时可以进一步缓解相似负样本的干扰问题。大量实验验证了该方法在许多基准数据集上的优越性。
URL
https://arxiv.org/abs/1804.03864
