Datasets & Competitions
The Semantic Highlight Mask Dataset holds significant importance in the field of computer vision, particularly for tasks such as object detection, image segmentation, and scene understanding. This paper proposes a novel Semantic Highlight Mask Dataset designed to support high-precision semantic segmentation and emphasize key regions in images. The dataset encompasses a diverse range of image categories, with finely annotated highlight masks assigning semantic labels and saliency weights to each pixel.
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The Semantic Highlight Mask Dataset holds significant importance in the field of computer vision, particularly for tasks such as object detection, image segmentation, and scene understanding. This paper proposes a novel Semantic Highlight Mask Dataset designed to support high-precision semantic segmentation and emphasize key regions in images. The dataset encompasses a diverse range of image categories, with finely annotated highlight masks assigning semantic labels and saliency weights to each pixel.
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PEMS03, PEMS04, PEMS07 and PEMS08, these four datasets are constructed from four areas in California. All the data was collected from the Caltrans Performance Measurement System (PeMS) and the spatial adjacency matrices for each dataset were built using a distance-based real road network. PEMS03 has 358 sensors with a duration of 3 months. PEMS04 has 307 sensors with a duration of 2 months. PEMS07 has 883 sensors with a duration of 3 months. PEMS08 has 170 sensors with a duration of 2 months.
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