Few-shot fine-grained classification with Spatial Attentive Comparison

• Main Authors: Ruan, Xiaoqian, Lin, Guosheng, Long, Cheng, Lu, Shengli
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Few-Shot Learning; Fine-Grained Classification
• Online Access: https://hdl.handle.net/10356/160696
• Institution: Nanyang Technological University

The main goal of this paper is to propose a novel model, named Spatial Attentive Comparison Network (SACN), which is used to address a problem, termed few-shot fine-grained recognition (FSFG). FSFG is to recognize fine-grained examples with only a few samples, which is challenging for deep neural ne...