Person search by natural language aims at retrieving a specific person in a
large-scale image pool that matches the given textual descriptions. While most
of the current methods treat the task as a holistic visual and textual feature
matching one, we approach it from an attribute-aligning perspective that allows
grounding specific attribute phrases to the corresponding visual regions. We
achieve success as well as the performance boosting by a robust feature
learning that the referred identity can be accurately bundled by multiple
attribute visual cues. To be concrete, our Visual-Textual Attribute Alignment
model (dubbed as ViTAA) learns to disentangle the feature space of a person
into subspaces corresponding to attributes using a light auxiliary attribute
segmentation computing branch. It then aligns these visual features with the
textual attributes parsed from the sentences by using a novel contrastive
learning loss. Upon that, we validate our ViTAA framework through extensive
experiments on tasks of person search by natural language and by
attribute-phrase queries, on which our system achieves state-of-the-art
performances. Code will be publicly available upon publication.

本文提出一种基于属性对齐的视觉 - 文本对齐方法 (ViTAA)，通过较轻的属性分割计算模块分离人物特征空间并使用对比学习损失函数对视觉和文本属性进行对齐，从而成功提升了自然语言下的人物搜索和属性 - 短语查询任务的表现，实现了最先进的结果。