Paper Info
Title
Fine-Grained Grocery Product Recognition by One-Shot Learning.
Abstract
Fine-grained grocery product recognition via camera is a challenging task to identify the visually similar products with subtle differences by using single-shot training examples. To address this issue? we present a novel hybrid classification approach that combines feature-based matching and one-shot deep learning with a coarse-to-fine strategy. The candidate regions of product instances are first detected and coarsely labeled by recurring features in product images without any training. Then, attention maps are generated to guide the classifier to focus on fine discriminative details by magnifying the influences of the features in the candidate regions of interest (ROI) and suppressing the interferences of the features outside, improving the accuracy of fine-grained grocery products recognition effectively. Our framework also performs a good adaptability which allows existing classifier to be refined without retraining for new coming product classes. As an additional contribution, we collect a new grocery product database with 102 classes from 2 stores. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods.
Year
DOI
Venue
2018
10.1145/3240508.3240522
MM '18: ACM Multimedia Conference Seoul Republic of Korea October, 2018
Keywords
Field
DocType
Product Categorization, Fine-Grained Object Recognition
Adaptability,Computer vision,Computer science,Artificial intelligence,Deep learning,One-shot learning,Classifier (linguistics),Discriminative model,Machine learning,Retraining
Conference
ISBN
Citations 
PageRank 
978-1-4503-5665-7
0
0.34
References 
Authors
31
9
Name
Order
Citations
PageRank
Weidong Geng116221.81
Feilin Han2253.61
Jiangke Lin321.04
Liuyi Zhu400.34
Jieming Bai500.34
suzhen wang635.12
Lin He7195.37
Qiang Xiao800.34
Zhangjiong Lai900.34