Zero-shot Composed Image Retrieval Considering Query-target Relationship Leveraging Masked Image-text Pairs

Autor:	Zhang, Huaying, Yanagi, Rintaro, Togo, Ren, Ogawa, Takahiro, Haseyama, Miki
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted as a conference paper in IEEE ICIP 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.18836 Zobrazit plný text záznamu View this record from Arxiv