2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMMSP-8.3
Paper Title HIERARCHICAL SIMILARITY LEARNING FOR LANGUAGE-BASED PRODUCT IMAGE RETRIEVAL
Authors Zhe Ma, Fenghao Liu, Zhejiang University, China; Jianfeng Dong, Zhejiang Gongshang University, China; Xiaoye Qu, Huazhong University of Science and Technology, China; Yuan He, Alibaba Group, China; Shouling Ji, Zhejiang University, China
SessionMMSP-8: Multimedia Retrieval and Signal Detection
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Multimedia Signal Processing: Multimedia Databases and File Systems
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.