KINS dataset for amodal instance segmentation
Amodal instance segmentation, a new direction of instance segmentation, aims to segment each object instance involving its invisible, occluded parts to imitate human ability. This task requires to reason objects’ complex structure. Despite important and futuristic, this task lacks data with large-scale and detailed annotation, due to the difficulty of correctly and consistently labeling invisible parts, which creates the huge barrier to explore the frontier of visual recognition. We augment KITTI with more instance pixel-level annotation for 8 categories, which we call KITTI INStance dataset (KINS).
Portraiture is a major art form in both photography and painting. In most instances, artists seek to make the subject stand out from its surrounding, for instance, by making it brighter or sharper. In the digital world, similar effects can be achieved by processing a portrait image with photographic or painterly filters that adapt to the semantics of the image. While many successful user-guided methods exist to delineate the subject, fully automatic techniques are lacking and yield unsatisfactory results. We first address this problem by introducing a new automatic segmentation algorithm dedicated to portraits. We then build upon this result and describe several portrait filters that exploit our automatic segmentation algorithm to generate high-quality portraits.
Deep Vision Lab
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