@inproceedings{b48f544d142a44898cefa53cb2d99cd9,
title = "Supervised two-stage transfer learning on imbalanced dataset for sport classification",
abstract = "Sport classification is a crucial step for content analysis in a sport stream monitoring system. Training a reliable sport classifier can be a challenging task when the data is limited in amount and highly imbalanced. In this paper, we introduce a supervised two-stage transfer learning (Two-Stage-TL) method to solve the data shortage problem. It can progressively transfer features from a source domain to the target domain using a properly selected bridge domain. For the class imbalance issue, we compare several existing methods and demonstrate that the log-smoothing class weight is the most applicable way for this specific problem. Extensive experiments are conducted using ResNet50, VGG16, and Inception-ResNet-v2. The results show that Two-Stage-TL outperforms classical One-Stage-TL and achieves the best performance using log-smoothing class weight. The in-depth analysis is useful for researchers and developers in solving similar problems.",
keywords = "Class imbalance learning, Multimedia content analysis, Sport classification, Transfer learning",
author = "Tianyu Bi and Dmitri Jarnikov and Johan Lukkien",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-30642-7_32",
language = "English",
isbn = "978-3-030-30641-0",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "356--366",
editor = "Elisa Ricci and Nicu Sebe and {Rota Bul{\`o}}, Samuel and Cees Snoek and Oswald Lanz and Stefano Messelodi",
booktitle = "Image Analysis and Processing – ICIAP 2019 - 20th International Conference, Proceedings",
address = "Germany",
note = "20th International Conference on Image Analysis and Processing, ICIAP 2019 ; Conference date: 09-09-2019 Through 13-09-2019",
}