2014/04/17 by Jiang Wang, Yang song, Wang, Jiang +14 · 13 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1404.4661
CVPR 2014
arxiv created 2014/04/17 · openalex publication_date 2014/04/17 · arxiv updated 2014/04/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images.It has higher learning capability than models based on hand-crafted features. A novel multiscale network structure has been developed to describe the images effectively. An efficient triplet sampling algorithm is proposed to learn the model with distributed asynchronized stochastic gradient. Extensive experiments show that the proposed algorithm outperforms models based on hand-crafted visual features and deep classification models.