A feature consistency driven attention erasing network for fine-grained image retrieval

作者：

Highlights：

• Feature consistency driven attention erasing network (FCAENet) is designed to learn a more representative hash code and preserve pair-wise similarity better.

• Selective region erasing module (SREM) is a novel data augmentation method to make the feature extractor more robust for large-scale fine-grained image retrieval.

• Enhance space relation loss (ESRL) is employed to make the query hash code more relative to the database hash code for improving the retrieval performance.

• Abundant experiments on fine-grained datasets have been done and we achieve the stateof-the-art (SOTA) results for fine-grained image retrieval.

摘要

•Feature consistency driven attention erasing network (FCAENet) is designed to learn a more representative hash code and preserve pair-wise similarity better.•Selective region erasing module (SREM) is a novel data augmentation method to make the feature extractor more robust for large-scale fine-grained image retrieval.•Enhance space relation loss (ESRL) is employed to make the query hash code more relative to the database hash code for improving the retrieval performance.•Abundant experiments on fine-grained datasets have been done and we achieve the stateof-the-art (SOTA) results for fine-grained image retrieval.

论文关键词：Fine-grained image retrieval,Deep hashing learning,Selective region erasing module,Feature consistency

论文评审过程：Received 11 October 2021, Revised 12 January 2022, Accepted 28 February 2022, Available online 2 March 2022, Version of Record 23 March 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108618