Deep Adversarial Graph Attention Convolution Network for Text-Based Person Search

The newly emerging text-based person search task aims at retrieving the target pedestrian by a query in natural language with fine-grained description of a pedestrian. It is more applicable in reality without the requirement of image/video query of a pedestrian, as compared to image/video based person search, i.e., person re-identification. In this work, we propose a novel deep adversarial graph attention convolution network (A-GANet) for text-based person search. The A-GANet exploits both textual and visual scene graphs, consisting of object properties and relationships, from the text queries and gallery images of pedestrians, towards learning informative textual and visual representations. It learns an effective joint textual-visual latent feature space in adversarial learning manner, bridging modality gap and facilitating pedestrian matching. Specifically, the A-GANet consists of an image graph attention network, a text graph attention network and an adversarial learning module. The image and text graph attention networks are designed with a novel graph attention convolution layer, which effectively exploits graph structure in the learning of textual and visual features, leading to precise and discriminative representations. An adversarial learning module is developed with a feature transformer and a modality discriminator, to learn a joint textual-visual feature space for cross-modality matching. Extensive experimental results on two challenging benchmarks, i.e., CUHK-PEDES and Flickr30k datasets, have demonstrated the effectiveness of the proposed method.