The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
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This referring expression generation (REG) dataset was collected using the ReferitGame. In this two-player game, the first player is shown an image with a segmented target object and asked to write a natural language expression referring to the target object. The second player is shown only the image and the referring expression and asked to click on the corresponding object. If the players do their job correctly, they receive points and swap roles. If not, they are presented with a new object and image for description. Images in these collections were selected to contain two or more objects of the same object category. In the RefCOCO dataset, no restrictions are placed on the type of language used in the referring expressions. In a version of this dataset called RefCOCO+ players are disallowed from using location words in their referring expressions by adding “taboo” words to the ReferItGame. This dataset was collected to obtain a referring expression dataset focsed on purely appearan
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The ABC Dataset is a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms.
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MobilityAids is a dataset for perception of people and their mobility aids. The annotated dataset contains five classes: pedestrian, person in wheelchair, pedestrian pushing a person in a wheelchair, person using crutches and person using a walking frame. In total the hospital dataset has over 17, 000 annotated RGB-D images, containing people categorized according to the mobility aids they use. The images were collected in the facilities of the Faculty of Engineering of the University of Freiburg and in a hospital in Frankfurt.
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