论文标题
学习通过累积的时间差异来对图像序列进行排序
Learning to Sort Image Sequences via Accumulated Temporal Differences
论文作者
论文摘要
考虑一组带有静态或手持相机捕获的动态物体的场景的N图像。让捕获这些图像的时间顺序未知。可以有n！这些图像可以捕获的时间顺序的可能性。在这项工作中，我们解决了暂时测序使用手持相机捕获的动态场景的一组无序图像的问题。我们提出了一个卷积块，该块通过2D卷积内核捕获空间信息，并通过利用从输入图像中提取的特征图中存在的差异来捕获时间信息。我们评估了从标准操作识别数据集UCF101提取的数据集上提出的方法的性能。我们表明，所提出的方法的表现优于最新方法。我们表明，网络通过从戴维斯数据集提取的数据集上进行评估，这是一个用于视频对象分割的数据集，当时使用从UCF101提取的数据集进行了培训，该数据集是用于操作识别的数据集中的数据集。
Consider a set of n images of a scene with dynamic objects captured with a static or a handheld camera. Let the temporal order in which these images are captured be unknown. There can be n! possibilities for the temporal order in which these images could have been captured. In this work, we tackle the problem of temporally sequencing the unordered set of images of a dynamic scene captured with a hand-held camera. We propose a convolutional block which captures the spatial information through 2D convolution kernel and captures the temporal information by utilizing the differences present among the feature maps extracted from the input images. We evaluate the performance of the proposed approach on the dataset extracted from a standard action recognition dataset, UCF101. We show that the proposed approach outperforms the state-of-the-art methods by a significant margin. We show that the network generalizes well by evaluating it on a dataset extracted from the DAVIS dataset, a dataset meant for video object segmentation, when the same network was trained with a dataset extracted from UCF101, a dataset meant for action recognition.
