The Temporal Shape Dataset
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To be able to study both temporal modeling abilities and cross-domain-robustness in a light-weight manner, we propose the Temporal Shape dataset. It is a synthetically created dataset for classification of short clips showing either a square dot or a MNIST digit tracing shapes with their trajectories over time. The dataset has five different trajectory classes (i.e., temporal shapes): circle, line, arc, spiral or rectangle. The task is to recognize which class was drawn by the moving entity across the frames of the sequence. The spatial appearance of the moving object is not correlated with the temporal shape class, and can thus not be employed in the recognition. In the first three domains (2Dot, 5Dot, MNIST), the background is black, and in the last domain (MNIST-bg), the background contains white Perlin noise. The Perlin noise can be more or less fine-grained; scale is regulated by a random parameter. The dataset can be thought of as a heavily scaled-down version of an action template dataset, such as 20BN-Something-something-v2 (Goyal et al., ICCV 2017), entirely stripped of appearance cues. See README.txt for more information on the different zip-files that constitute the dataset.
为以轻量级方式同时研究时序建模能力与跨域鲁棒性，我们提出了时序形状数据集（Temporal Shape Dataset）。该数据集为人工合成数据集，用于分类展示随时间推移以轨迹绘制图形的短视频片段，片段中的移动实体可为方形光点或MNIST手写数字。该数据集包含五种轨迹类别（即时序形状）：圆形、直线、圆弧、螺旋线与矩形。任务目标为识别移动实体在序列帧中绘制的轨迹类别。移动物体的空间外观与时序形状类别无关联，因此无法作为识别依据。在前三个数据集域（2Dot、5Dot、MNIST）中，背景为纯黑色；而在最后一个域（MNIST-bg）中，背景带有白色柏林噪声（Perlin Noise）。柏林噪声的精细程度可调，其缩放比例由随机参数控制。该数据集可被视为动作模板数据集（如20BN-Something-something-v2，Goyal等，ICCV 2017）的大幅缩版，完全剥离了外观线索。有关构成该数据集的各压缩包的详细信息，请参阅README.txt。
