Log In Sign Up

Hierarchical Similarity Learning for Language-based Product Image Retrieval

by   Zhe Ma, et al.

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


Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing

Due to the availability of large-scale multi-modal data (e.g., satellite...

Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing

The development of cross-modal retrieval systems that can search and ret...

OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval

Retrieving tracked-vehicles by natural language descriptions plays a cri...

Scene Text Retrieval via Joint Text Detection and Similarity Learning

Scene text retrieval aims to localize and search all text instances from...

Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information

Cross-modal remote sensing text-image retrieval (RSCTIR) has recently be...

Correlation Verification for Image Retrieval

Geometric verification is considered a de facto solution for the re-rank...

Segment Augmentation and Differentiable Ranking for Logo Retrieval

Logo retrieval is a challenging problem since the definition of similari...