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The ADE20K semantic segmentation dataset contains more than 20K scene-centric images exhaustively annotated with pixel-level objects and object parts labels. There are totally 150 semantic categories, which include stuffs like sky, road, grass, and discrete objects like person, car, bed. Source: Cooperative Image Segmentation and Restoration in Adverse Environmental Conditions Image Source: https://groups.csail.mit.edu/vision/datasets/ADE20K/
A high quality sound prompted semantic segmentation dataset
A high quality speech prompted semantic segmentation dataset
A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [DS] employs downsampled semantic maps that are resized by nearest-neighbor interpolation, simulating human errors e.g., jagged edges and coarse/low resolution user inputs.
A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [Edge] masks the edges of instances with an unlabeled class, imitating incomplete annotations around edges, especially between instances.
A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [Random] randomly adds an unlabeled class to the semantic maps, mimicing unintended user error and extreme random noise.