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Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training...
Land-cover and land-use semantic labeling in centimeter resolution imagery (ultra-high resolution) is mostly performed by supervised classification of informative descriptors extracted from spatially coherent but small objects (e.g. superpixels or patches). In this paper, we propose an extension of this reasoning by proposing a class-specific, multi-scale and bottom-up object proposal strategy to...
Semantic interpretation of the environment can significantly improve the capabilities of our autonomous robots. This work is focused on automatic semantic label propagation in video of indoor environments acquired by a mobile robot. Using a small number of training examples, we propose a new approach to recognize and label dominant background regions of interest, such as floor, wall and doors, and...
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