Semantic Object Selection

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Deep Extreme Cut: From Extreme Points to Object Segmentation (2017, methodology): “Image-level labels [26], noisy web labels [1, 16] and scribble-level labels [20] are some of the supervisory signal that have been used to guide segmentation methods.”
What's the Point: Semantic Segmentation with Point Supervision (2015, background): “Many forms of supervision have been explored, such as eye tracks [25], free-form squiggles [41], noisy web tags [1], or heterogeneous annotations (a large number of image-level labels and a small number of full segmentation masks) [17].”
Webly Supervised Semantic Segmentation (2017, methodology): “Webly Supervised Computer Vision The idea of utilizing web images for supervising computer vision algorithms has been explored in several tasks, such as object classification [28], object detection [29], object parts localization [30] and object segmentation [17] [31] [32] [33].”
Multi-instance object segmentation with occlusion handling (2015, methodology): “Then, the inferred occluded regions, shape predictions and classspecific likelihood maps are formulated into an energy minimization framework [30, 27, 1] to obtain the desired segmentation candidates (e.”