Person text-image matching, also known as text based person search, aims to
retrieve images of specific pedestrians using text descriptions. Although
person text-image matching has made great research progress, existing methods
still face two challenges. First, the lack of interpretability of text features
makes it challenging to effectively align them with their corresponding image
features. Second, the same pedestrian image often corresponds to multiple
different text descriptions, and a single text description can correspond to
multiple different images of the same identity. The diversity of text
descriptions and images makes it difficult for a network to extract robust
features that match the two modalities. To address these problems, we propose a
person text-image matching method by embedding text-feature interpretability
and an external attack node. Specifically, we improve the interpretability of
text features by providing them with consistent semantic information with image
features to achieve the alignment of text and describe image region this http URL
address the challenges posed by the diversity of text and the corresponding
person images, we treat the variation caused by diversity to features as caused
by perturbation information and propose a novel adversarial attack and defense
method to solve it. In the model design, graph convolution is used as the basic
framework for feature representation and the adversarial attacks caused by text
and image diversity on feature extraction is simulated by implanting an
additional attack node in the graph convolution layer to improve the robustness
of the model against text and image diversity. Extensive experiments
demonstrate the effectiveness and superiority of text-pedestrian image matching
over existing methods. The source code of the method is published at

该研究提出了一种人物文本 - 图像匹配方法，它通过嵌入文本特征可解释性和外部攻击节点，采用图卷积作为特征表示的基本框架，并模拟了文本和图像多样性对特征提取的对抗攻击和防御来提高模型的鲁棒性，从而有效解决了现有方法面临的挑战。