Hierarchical Similarity Learning for Language-based Product Image Retrieval

• URL: http://arxiv.org/abs/2102.09375v1
• Date: Thu, 18 Feb 2021 14:23:16 GMT
• Title: Hierarchical Similarity Learning for Language-based Product Image Retrieval
• Authors: Zhe Ma, Fenghao Liu, Jianfeng Dong, Xiaoye Qu, Yuan He, Shouling Ji
• Abstract summary: This paper focuses on the cross-modal similarity measurement, and proposes a novel Hierarchical Similarity Learning network. Experiments on a large-scale product retrieval dataset demonstrate the effectiveness of our proposed method.
• Score: 40.83290730640458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper aims for the language-based product image retrieval task. The majority of previous works have made significant progress by designing network structure, similarity measurement, and loss function. However, they typically perform vision-text matching at certain granularity regardless of the intrinsic multiple granularities of images. In this paper, we focus on the cross-modal similarity measurement, and propose a novel Hierarchical Similarity Learning (HSL) network. HSL first learns multi-level representations of input data by stacked encoders, and object-granularity similarity and image-granularity similarity are computed at each level. All the similarities are combined as the final hierarchical cross-modal similarity. Experiments on a large-scale product retrieval dataset demonstrate the effectiveness of our proposed method. Code and data are available at https://github.com/liufh1/hsl.

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