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用于上下文感知类别发现的对象图
Object-Graphs for Context-Aware Category Discovery |
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| 课程网址: |
http://videolectures.net/cvpr2010_lee_ogca/
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| 主讲教师: |
Yong Jae Lee
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| 开课单位: |
德克萨斯大学
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| 开课时间: |
2010-07-19
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| 课程语种: |
英语
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| 中文简介: |
如何了解某些类别有助于我们在未标记的图像中揭示新的类别？无监督的视觉类别发现对于在没有人工监督的情况下挖掘重复对象是有用的，但是现有方法假定先前信息并因此倾向于对具有多个对象的混乱场景执行不良。我们建议利用有关以前学过的类别的年龄知识，以便能够更准确地发现。我们引入了一种新颖的对象图描述符来编码相对于不熟悉区域的对象级别共现模式的布局，并且通过使用它来模拟图像的已知和未知对象之间的交互，我们可以更好地识别新的视觉类别。我们的方法不是从头开始挖掘所有的猫类，而是利用熟悉的有用线索来识别新对象。我们在基准数据集上评估我们的方法，并证明在发现方面比传统的基于纯粹外观的基线有明显的改进。
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| 课程简介: |
How can knowing about some categories help us to dis- cover new ones in unlabeled images? Unsupervised visual category discovery is useful to mine for recurring objects without human supervision, but existing methods assume no prior information and thus tend to perform poorly for cluttered scenes with multiple objects. We propose to lever- age knowledge about previously learned categories to en- able more accurate discovery. We introduce a novel object- graph descriptor to encode the layout of object-level co- occurrence patterns relative to an unfamiliar region, and show that by using it to model the interaction between an image’s known and unknown objects we can better de- tect new visual categories. Rather than mine for all cat- egories from scratch, our method identifies new objects while drawing on useful cues from familiar ones. We eval- uate our approach on benchmark datasets and demonstrate clear improvements in discovery over conventional purely appearance-based baselines.
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| 关 键 词: |
视觉类别; 对象图; 共现模式
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-10：yumf
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| 阅读次数: |
23
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