Large-Scale Logo Detection
文献类型：期刊论文
| 作者 | Hou, Sujuan1,2; Li, Jiacheng1; Min, Weiqing3; Zhan, Jianxin1; Zhang, Mengmeng2; Li, Peng1; Jiang, Shuqiang3 |
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2026-03-01 |
| 卷号 | 48期号:3页码:2919-2935 |
| 关键词 | Benchmark testing Training Visualization Annotations Deep learning Videos Transformers Monitoring Feature extraction Detectors Logo detection large-scale dataset deep learning |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2025.3630505 |
| 英文摘要 | Logo detection is crucial for trademark compliance and media monitoring, enabling companies to monitor online trademark usage and evaluate brand visibility on social media and advertisements. The use of large datasets significantly improves accuracy and generalization, emphasizing the need for high-quality datasets to optimize performance and enhance reasoning abilities in visual detection models. This drove us to create Logo4500, an unparalleled dataset featuring 4,500 logo categories and over 293,000 meticulously labeled images. To ensure the dataset's quality, we meticulously designed the construction and annotation process, with detailed information provided in our paper. Compared to existing logo datasets, Logo4500 offers greater diversity and class imbalance, making it more reflective of real-world distribution. Leveraging this high-quality dataset, we introduce a benchmark called Frequency-Aware Learnable Dual Reweighting Network (FALDR-Net), which enhances the representation of ambiguous features and addresses class imbalance for large-scale logo detection. We conducted extensive experiments, evaluating various recent methods on this new dataset and several existing publicly available logo datasets, demonstrating its effectiveness. Additionally, we verified Logo4500's generalization ability in several tasks. We anticipate that Logo4500 and the benchmark will inspire further exploration in the logo-related research community, facilitating the advancement of visual foundation models. |
| 资助项目 | National Nature Science Foundation of China[62372278] ; National Nature Science Foundation of China[62473055] ; National Nature Science Foundation of China[62125207] ; National Nature Science Foundation of China[62472411] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001680997300040 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42793]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Hou, Sujuan; Min, Weiqing; Zhang, Mengmeng |
| 作者单位 | 1.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China 2.Beijing Union Univ, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hou, Sujuan,Li, Jiacheng,Min, Weiqing,et al. Large-Scale Logo Detection[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2026,48(3):2919-2935. |
| APA | Hou, Sujuan.,Li, Jiacheng.,Min, Weiqing.,Zhan, Jianxin.,Zhang, Mengmeng.,...&Jiang, Shuqiang.(2026).Large-Scale Logo Detection.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,48(3),2919-2935. |
| MLA | Hou, Sujuan,et al."Large-Scale Logo Detection".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 48.3(2026):2919-2935. |
入库方式： OAI收割
来源：计算技术研究所
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