A Multi-scale Metric Learning Approach for Product Retrieval
Product image retrieval is a typical large-scale metric learning task.The specificity of this task is that the commodity retail platform needs to import new types of items regularly,and the appearances of existing products also change from time to time.The previous work show that although the traditional metric learning can extend the recognition range of product retrieval to unseen product types,the performance of traditional metric learning in product retrieval is still limited,because it can only use a single scale of regulatory information to deal with such large-scale retrieval problems.Therefore,we propose a product retrieval method based on multi-scale deep metric learning.The proposed method uses the label information of multiple scales to train the model and adopts the co-attention module to integrate the deep features of different scales effectively,which can improve the ability of the model to obtain important information and effectively improve the retrieval performance of the deep learning model at the fine-grained level.The proposed method achieves 43.0%and 65.9%on mAP and Rank-1 in experiments on large-scale product retrieval dataset,improved by 6.4%and 7.8%,respectively compared with the traditional metric learning.
国家科技期刊平台
NSTL
万方数据
维普
