Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. In recent years, TBPS has made remarkable progress and state-of-the-art methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities, which is unreliable due to the lack of contextual information or the potential introduction of noise. Moreover, existing methods seldom consider the information inequality problem between modalities caused by image-specific information. To address these limitations, we propose an efficient joint Multi-level Alignment Network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels, and realize fast and effective person search. Specifically, we first design an image-specific information suppression module, which suppresses image background and environmental factors by relation-guided localization and channel attention filtration respectively. This module effectively alleviates the information inequality problem and realizes the alignment of information volume between images and texts. Secondly, we propose an implicit local alignment module to adaptively aggregate all pixel/word features of image/text to a set of modality-shared semantic topic centers and implicitly learn the local fine-grained correspondence between modalities without additional supervision and cross-modal interactions. And a global alignment is introduced as a supplement to the local perspective. The cooperation of global and local alignment modules enables better semantic alignment between modalities. Extensive experiments on multiple databases demonstrate the effectiveness and superiority of our MANet.

翻译：文本行人检索是一项具有挑战性的任务，旨在根据查询文本从图像库中搜索具有相同身份的行人图像。近年来，文本行人检索取得了显著进展，现有最先进方法通过学习图像与文本之间的局部细粒度对应关系实现了优越性能。然而，大多数现有方法依赖显式生成的局部部件来建模模态间的细粒度对应关系，由于缺乏上下文信息或可能引入噪声，这种建模方式并不可靠。此外，现有方法很少考虑由图像特定信息引起的模态间信息不平等问题。为解决这些局限性，我们提出了一种高效的联合多层级对齐网络（MANet）用于文本行人检索，该网络能在多个层级上学习对齐的图像/文本特征表示，并实现快速有效的行人检索。具体而言，我们首先设计了一个图像特定信息抑制模块，该模块分别通过关系引导定位和通道注意力过滤来抑制图像背景与环境因素。该模块有效缓解了信息不平等问题，实现了图像与文本间信息量的对齐。其次，我们提出了隐式局部对齐模块，该模块能自适应地将图像/文本的所有像素/词特征聚合到一组模态共享的语义主题中心，并在无需额外监督和跨模态交互的情况下隐式学习模态间的局部细粒度对应关系。同时引入全局对齐作为局部视角的补充。全局与局部对齐模块的协同作用实现了模态间更好的语义对齐。在多个数据库上的广泛实验证明了我们MANet的有效性与优越性。