现实生活中常常会有这样的问题:缺乏足够的先验知识,因此难以人工标注类别或进行人工类别标注的成本太高。很自然地,我们希望计算机能代我们完成这些工作,或至少提供一些帮助。根据类别未知(没有被标记)的训练样本解决模式识别中的各种问题,称之为无监督学习

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无监督多对象表示学习依赖于归纳偏差来指导发现以对象为中心的表示。然而,我们观察到,学习这些表征的方法要么是不切实际的,因为长时间的训练和大量的记忆消耗,要么是放弃了关键的归纳偏见。在这项工作中,我们引入了EfficientMORL,一个有效的无监督学习框架的对象中心表示。我们证明了同时要求对称性和解缠性所带来的优化挑战实际上可以通过高成本的迭代摊销推理来解决,通过设计框架来最小化对它的依赖。我们采用两阶段的方法进行推理:首先,分层变分自编码器通过自底向上的推理提取对称的解缠表示,其次,轻量级网络使用自顶向下的反馈来改进表示。在训练过程中所采取的细化步骤的数量根据课程减少,因此在测试时零步骤的模型达到了99.1%的细化分解性能。我们在标准多目标基准上演示了强大的对象分解和解缠,同时实现了比以前最先进的模型快一个数量级的训练和测试时间推断。

https://www.zhuanzhi.ai/paper/f29b88ee56208601f787cc791e3c7414

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In today's world, the amount of data produced in every field has increased at an unexpected level. In the face of increasing data, the importance of data processing has increased remarkably. Our resource topic is on the processing of video data, which has an important place in increasing data, and the production of summary videos. Within the scope of this resource, a new method for anomaly detection with object-based unsupervised learning has been developed while creating a video summary. By using this method, the video data is processed as pixels and the result is produced as a video segment. The process flow can be briefly summarized as follows. Objects on the video are detected according to their type, and then they are tracked. Then, the tracking history data of the objects are processed, and the classifier is trained with the object type. Thanks to this classifier, anomaly behavior of objects is detected. Video segments are determined by processing video moments containing anomaly behaviors. The video summary is created by extracting the detected video segments from the original video and combining them. The model we developed has been tested and verified separately for single camera and dual camera systems.

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In today's world, the amount of data produced in every field has increased at an unexpected level. In the face of increasing data, the importance of data processing has increased remarkably. Our resource topic is on the processing of video data, which has an important place in increasing data, and the production of summary videos. Within the scope of this resource, a new method for anomaly detection with object-based unsupervised learning has been developed while creating a video summary. By using this method, the video data is processed as pixels and the result is produced as a video segment. The process flow can be briefly summarized as follows. Objects on the video are detected according to their type, and then they are tracked. Then, the tracking history data of the objects are processed, and the classifier is trained with the object type. Thanks to this classifier, anomaly behavior of objects is detected. Video segments are determined by processing video moments containing anomaly behaviors. The video summary is created by extracting the detected video segments from the original video and combining them. The model we developed has been tested and verified separately for single camera and dual camera systems.

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