Conditional Cross Attention Network for Multi-Space Embedding without
  Entanglement in Only a SINGLE Network
Chull Hwan Song Taebaek Hwang Jooyoung Yoon Shunghyun Choi Yeong Hyeon Gu
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.