Adversarial Attribute-Text Embedding for Person Search with Natural Language Query

The newly emerging task of person search with natural language query aims at retrieving the target pedestrian by a text description of the pedestrian. It is more applicable compared to person search with image/video query, i.e., person re-identification. In this paper, we propose a novel Adversarial Attribute-Text Embedding (AATE) network for person search with text query. In particular, a cross-modal adversarial learning module is proposed to learn discriminative and modality-invariant visual-textual features. It consists of a cross-modal learner and a modality discriminator, playing a min-max game in an adversarial learning way. The former is to improve intra-modality discrimination and inter-modality invariance towards confusing the modality discriminator. The latter is to distinguish the features from different modalities and boost the learning of modality-invariant features. Moreover, a visual attribute graph convolutional network is proposed to learn visual attributes of pedestrians, which possess better descriptiveness, interpretability and robustness compared to pedestrian appearance features. A hierarchical text embedding network, consisting of multi-stacked bidirectional LSTMs and a textual attention block, is developed to extract effective textual features from text descriptions of pedestrians. Extensive experimental results on two challenging benchmarks, have demonstrated the effectiveness of the proposed approach.