图 书 馆
美国有线电视新闻网（CNN）图像检索从BoW学习：无监督微调（带硬例子）CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples |
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| 课程网址: | http://videolectures.net/eccv2016_radenovic_cnn_image/ |
| 主讲教师: | Filip Radenović |
| 开课单位: | 布拉格捷克技术大学 |
| 开课时间: | 2016-10-24 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in many computer vision tasks. However, this achievement is preceded by extreme manual annotation in order to perform either training from scratch or fine-tuning for the target task. In this work, we propose to fine-tune CNN for image retrieval from a large collection of unordered images in a fully automated manner. We employ state-of-the-art retrieval and Structure-from-Motion (SfM) methods to obtain 3D models, which are used to guide the selection of the training data for CNN fine-tuning. We show that both hard positive and hard negative examples enhance the final performance in particular object retrieval with compact codes. |
| 关 键 词: | 神经网络; 手动注释; 检索图像 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-22：chenxin01 |
| 最后编审: | 2023-05-22：chenxin01 |
| 阅读次数: | 54 |