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DBFC-Net: a uniform framework for fine-grained cross-media retrieval

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Abstract

With the growth of various types of media data (e.g., text, image, video, and audio), fine-grained cross-media retrieval which aims to provide flexible and accurate query service has attracted significant attention. Different from traditional keyword-based retrieval, the queries and results in fine-grained cross-media retrieval might be different types. In this work, we demonstrate that the quality of retrieval results can be further improved by additionally considering the media-specified information. Besides, the process of feature extraction should be different for queries with various media types. To this end, we propose a novel network architecture, namely Double Branch Fine-grained Cross-media Net (DBFC-Net), which is the first work that can use the media-specific information to construct the common features by a uniform framework. Furthermore, we devise an effective distance metric (cosine+) for fine-grained cross-media retrieval. Compared with commonly-used metrics (e.g., cosine function), our proposed cosine+ metric is well adaptive to handle fine-grained retrieval scenarios. Extensive experiments and ablation studies on publicly available datasets demonstrate the effectiveness of our proposed approach.

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Authors and Affiliations

  1. School of computer science and engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Qiong Wang, Youdong Guo & Yazhou Yao

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  1. Qiong Wang
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  2. Youdong Guo
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  3. Yazhou Yao
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Correspondence to Yazhou Yao.

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Wang, Q., Guo, Y. & Yao, Y. DBFC-Net: a uniform framework for fine-grained cross-media retrieval. Multimedia Systems 28, 423–432 (2022). https://doi.org/10.1007/s00530-021-00825-2

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