We construct the first large-scale dataset, USIS10K, for the underwater salient instance segmentation task, which contains 10,632 images and pixel-level annotations of 7 categories. As far as we know, this is the largest salient instance segmentation dataset, and includes Class-Agnostic and Multi-Class labels simultaneously.
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A set of realistic odd-one-out stimuli gathered "in the wild". Each image in the Odd-One-Out (O3) dataset depicts a scene with multiple objects similar to each other in appearance (distractors) and a singleton (target) distinct in one or more feature dimensions (e.g. color, shape, size). All images are resized so that the larger dimension is 1024px. Targets represent approx. 400 common object types such as flowers, sweets, chicken eggs, leaves, tiles and birds. Pixelwise masks are provided for targets and distractors. Annotations are generated using CVAT.
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A set of patterns used in psychophysical research to evaluate the ability of saliency algorithms to find targets distinct from distractors in orientation, color and size. Each image is a 7x7 grid and contains a single target. All images are 1024x1024px and have corresponding ground truth masks for the target and distractors.
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There exist several datasets for saliency detection, but none of them is specifically designed for high-resolution salient object detection. High-Resolution Salient Object Detection (HRSOD) dataset, containing 1610 training images and 400 test images. The total 2010 images are collected from the website of Flickr with the license of all creative commons. Pixel-level ground truths are manually annotated by 40 subjects. The shortest edge of each image in HRSOD is more than 1200 pixels.
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