Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening

• URL: http://arxiv.org/abs/2303.07740v1
• Date: Tue, 14 Mar 2023 09:36:42 GMT
• Title: Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening
• Authors: Min Cao, Yang Bai, Jingyao Wang, Ziqiang Cao, Liqiang Nie, Min Zhang
• Abstract summary: Current image-text retrieval methods suffer from $N$-related time complexity. This paper presents a simple and effective keyword-guided pre-screening framework for the image-text retrieval.
• Score: 53.1711708318581
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Under the flourishing development in performance, current image-text retrieval methods suffer from $N$-related time complexity, which hinders their application in practice. Targeting at efficiency improvement, this paper presents a simple and effective keyword-guided pre-screening framework for the image-text retrieval. Specifically, we convert the image and text data into the keywords and perform the keyword matching across modalities to exclude a large number of irrelevant gallery samples prior to the retrieval network. For the keyword prediction, we transfer it into a multi-label classification problem and propose a multi-task learning scheme by appending the multi-label classifiers to the image-text retrieval network to achieve a lightweight and high-performance keyword prediction. For the keyword matching, we introduce the inverted index in the search engine and create a win-win situation on both time and space complexities for the pre-screening. Extensive experiments on two widely-used datasets, i.e., Flickr30K and MS-COCO, verify the effectiveness of the proposed framework. The proposed framework equipped with only two embedding layers achieves $O(1)$ querying time complexity, while improving the retrieval efficiency and keeping its performance, when applied prior to the common image-text retrieval methods. Our code will be released.

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