This paper proposes a novel zero-shot composed image retrieval (CIR) method
considering the query-target relationship by masked image-text pairs. The
objective of CIR is to retrieve the target image using a query image and a
query text. Existing methods use a textual inversion network to convert the
query image into a pseudo word to compose the image and text and use a
pre-trained visual-language model to realize the retrieval. However, they do
not consider the query-target relationship to train the textual inversion
network to acquire information for retrieval. In this paper, we propose a novel
zero-shot CIR method that is trained end-to-end using masked image-text pairs.
By exploiting the abundant image-text pairs that are convenient to obtain with
a masking strategy for learning the query-target relationship, it is expected
that accurate zero-shot CIR using a retrieval-focused textual inversion network
can be realized. Experimental results show the effectiveness of the proposed
method.

提出了一种考虑查询目标关系的零样本组合图像检索方法，通过遮蔽的图像文本对。通过利用遮蔽策略学习查询目标关系并训练旨在检索的文本反转网络，预期可以实现精确的零样本组合图像检索。实验结果显示了该方法的有效性。