In person search task, it is hard to retrieve the query persons undergoing large visual changes. To tackle this problem, we propose to exploit the context information to rectify the original individual similarity for better retrieval. To this end, we propose to model a query frame and a gallery frame as a graph pair, and then design the Siamese Residual Graph Convolutional Networks (SR-GCN) to aggregate context information to generate graph similarity as a complement of the original similarity. To model the relationships between context persons, we define the joint similarity adjacency matrix which assigns the proposed joint similarity as the edge weight to measure the contributions a context person makes to the aggregation. Therefore, the context node with a higher possibility to be a co-traveler of the target node makes more contributions to the matching of the target node. To further enhance the discriminative power of individual features, we also design a Random Proxy Center loss which explicitly constrains the intra-class variations to be smaller than the inter-class variations in the feature space and could make use of unlabeled samples. Experimental results on two public datasets show that our approach performs favorably against the state-of-the-art methods.

在人物搜索任务中，很难检索到视觉变化较大的查询人物。为了解决这个问题，我们建议利用上下文信息来纠正原始个体相似性，以便更好地检索。为此，我们建议将查询框架和图库框架建模为图对，然后设计 Siamese Residual Graph Convolutional Networks (SR-GCN) 来聚合上下文信息以生成图相似度作为原始相似度的补充。为了对上下文人员之间的关系进行建模，我们定义了联合相似性邻接矩阵，该矩阵将建议的联合相似性分配为边缘权重，以衡量上下文人员对聚合的贡献。所以，更有可能成为目标节点的同游者的上下文节点对目标节点的匹配贡献更大。为了进一步增强单个特征的判别能力，我们还设计了一个随机代理中心损失，它明确地将类内变化限制为小于特征空间中的类间变化，并且可以利用未标记的样本。在两个公共数据集上的实验结果表明，我们的方法与最先进的方法相比表现良好。