Address the Unseen Relationships: Attribute Correlations in Text Attribute Person Search.

Text attribute person search aims to identify the particular pedestrian by textual attribute information. Compared to person re- identification tasks which requires imagery samples as its query, text attribute person search is more useful under the circumstance where only witness is available. Most existing text attribute person search methods focus on improving the matching correlation and alignments by learning better representations of person-attribute instance pairs, with few consideration of the latent correlations between attributes. In this work, we propose a graph convolutional network (GCN) and pseudo-label-based text attribute person search method. Concretely, the model directly constructs the attribute correlations by label co- occurrence probability, in which the nodes are represented by attribute embedding and edges are by the filtered correlation matrix of attribute labels. In order to obtain better representations, we combine the cross-attention module (CAM) and the GCN. Furthermore, to address the unseen attribute relationships, we update the edge information through the instances through testing set with high predicted probability thus to better adapt the attribute distribution. Extensive experiments illustrate that our model outperforms the existing state-of-the-art methods on publicly available person search benchmarks: Market-1501 and PETA.