Many studies in vision tasks have aimed to create effective embedding spaces
for single-label object prediction within an image. However, in reality, most
objects possess multiple specific attributes, such as shape, color, and length,
with each attribute composed of various classes. To apply models in real-world
scenarios, it is essential to be able to distinguish between the granular
components of an object. Conventional approaches to embedding multiple specific
attributes into a single network often result in entanglement, where
fine-grained features of each attribute cannot be identified separately. To
address this problem, we propose a Conditional Cross-Attention Network that
induces disentangled multi-space embeddings for various specific attributes
with only a single backbone. Firstly, we employ a cross-attention mechanism to
fuse and switch the information of conditions (specific attributes), and we
demonstrate its effectiveness through a diverse visualization example.
Secondly, we leverage the vision transformer for the first time to a
fine-grained image retrieval task and present a simple yet effective framework
compared to existing methods. Unlike previous studies where performance varied
depending on the benchmark dataset, our proposed method achieved consistent
state-of-the-art performance on the FashionAI, DARN, DeepFashion, and Zappos50K
benchmark datasets.

我们提出了一种条件交叉注意力网络，通过一个单一的主干网络产生可以区分出对象多个具体属性的解缠多空间嵌入。该方法首先利用交叉注意力机制融合和切换条件信息，通过多样的可视化示例证明其有效性。其次，首次将视觉变换应用于细粒度图像检索任务，并提出了一个简单且有效的框架，与现有方法相比。与以往研究不同的是，我们的方法在 FashionAI、DARN、DeepFashion 和 Zappos50K 基准数据集上取得了始终如一的最先进性能。