Scholarlink

This article discusses our team’s success in securing first place in ICPR 2024 Multi-Modal Visual Pattern Recognition Challenge-Track 2 by applying data augmentation, focal loss, and model ensemble techniques. Data enhancement improves training data diversity, focal loss addresses class imbalance by focusing on challenging examples, and model ensembling combines predictions from multiple models for better performance. Together, these strategies lead to significant improvements in model accuracy and robustness, contributing to our high-ranking result. Code at https://github.com/chaoyuhao/ICPR24_competition.git .

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Multi-modal Object Detection with Data Augmentation, Focal Loss, and Model Ensembling

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Junyu Wu,
  • Yuhao Chao,
  • Jie Liu

摘要

This article discusses our team’s success in securing first place in ICPR 2024 Multi-Modal Visual Pattern Recognition Challenge-Track 2 by applying data augmentation, focal loss, and model ensemble techniques. Data enhancement improves training data diversity, focal loss addresses class imbalance by focusing on challenging examples, and model ensembling combines predictions from multiple models for better performance. Together, these strategies lead to significant improvements in model accuracy and robustness, contributing to our high-ranking result. Code at https://github.com/chaoyuhao/ICPR24_competition.git .