论文标题
检测和描述视觉流的变化
Detection and Description of Change in Visual Streams
论文作者
论文摘要
本文提出了一个分析视觉流的变化的框架：图像的有序序列，可能被大量的时间差距隔开。我们提出了一种新方法，将未标记的数据纳入培训中，以生成自然语言的变化描述。我们还开发了一个框架来估计视觉流的变化时间。我们使用学习的表示形式来进行变更证据和感知变化的一致性，并将它们结合在基于剪切的正则变更检测器中。与不依赖语言的方法相比，我们作为贡献的一部分释放的视觉流数据集的实验评估表明，由自然语言描述驱动的表示学习可显着提高变化检测准确性。
This paper presents a framework for the analysis of changes in visual streams: ordered sequences of images, possibly separated by significant time gaps. We propose a new approach to incorporating unlabeled data into training to generate natural language descriptions of change. We also develop a framework for estimating the time of change in visual stream. We use learned representations for change evidence and consistency of perceived change, and combine these in a regularized graph cut based change detector. Experimental evaluation on visual stream datasets, which we release as part of our contribution, shows that representation learning driven by natural language descriptions significantly improves change detection accuracy, compared to methods that do not rely on language.
