The Temporal Shape Dataset
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 b' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["temporal shape","appearance agnostic","temporal modeling","temporal modelling","temporal dependency","action recognition","fine-grained action recognition","action template","video classification","video model","spatiotemporal features"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 b' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 b
temporal shape; appearance agnostic; temporal modeling; temporal modelling; temporal dependency; action recognition; fine-grained action recognition; action template; video classification; video model; spatiotemporal features