图 书 馆
基于属性和比较属性的约束半监督学习Constrained Semi-Supervised Learning using Attributes and Comparative Attributes |
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| 课程网址: | http://videolectures.net/eccv2012_shrivastava_attributes/ |
| 主讲教师: | Stefan Carlsson, Antonio Torralba, Abhinav Shrivastava |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2012-11-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the problem of semi-supervised bootstrap learning for scene categorization. Existing semi-supervised approaches are typically unreliable and face semantic drift because the learning task is under-constrained. This is primarily because they ignore the strong interactions that often exist between scene categories, such as the common attributes shared across categories as well as the attributes which make one scene different from another. The goal of this paper is to exploit these relationships and constrain the semi-supervised learning problem. For example, the knowledge that an image is an auditorium can improve labeling of amphitheaters by enforcing constraint that an amphitheater image should have more circular structures than an auditorium image. We propose constraints based on mutual exclusion, binary attributes and comparative attributes and show that they help us to constrain the learning problem and avoid semantic drift. We demonstrate the effectiveness of our approach through extensive experiments, including results on a very large dataset of one million images. |
| 关 键 词: | 场景分类; 半监督引导学习; 语义漂移 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 84 |