In the domain of language-based fashion image retrieval, pinpointing the
desired fashion item using both a reference image and its accompanying textual
description is an intriguing challenge. Existing approaches lean heavily on
static fusion techniques, intertwining image and text. Despite their
commendable advancements, these approaches are still limited by a deficiency in
flexibility. In response, we propose a Self-distilled Dynamic Fusion Network to
compose the multi-granularity features dynamically by considering the
consistency of routing path and modality-specific information simultaneously.
Two new modules are included in our proposed method: (1) Dynamic Fusion Network
with Modality Specific Routers. The dynamic network enables a flexible
determination of the routing for each reference image and modification text,
taking into account their distinct semantics and distributions. (2) Self Path
Distillation Loss. A stable path decision for queries benefits the optimization
of feature extraction as well as routing, and we approach this by progressively
refine the path decision with previous path information. Extensive experiments
demonstrate the effectiveness of our proposed model compared to existing
methods.

在语言和时尚图像检索的领域中，使用参考图像和相应的文本描述来定位所需的时尚项目是一个有趣的挑战。本文提出了一种自我提取的动态融合网络，通过同时考虑路由路径的一致性和模态特定信息，动态地组合多粒度特征。我们的方法包括两个新模块：（1）具有模态特定路由器的动态融合网络，通过考虑参考图像和修改文本的不同语义和分布，实现了灵活的路由确定；（2）自我路径提取损失，通过使用先前路径信息逐步改进查询的路径决策，有利于特征提取和路由的优化。大量实验证明了我们提出的模型相对于现有方法的有效性。