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We aim to jointly estimate height and semantically label monocular aerial images. These two tasks are traditionally addressed separately in remote sensing, despite their strong correlation. Therefore, a model learning both height and classes jointly seems advantageous and so, we propose a multitask Convolutional Neural Network (CNN) architecture with two losses: one performing semantic labeling, and...
In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, semantic edge context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixel-wise categorization in an...
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...
In this paper, we propose an adaptive template for semantic labeling of indoor scene objects and estimating their oriented bounding facets (OBFs). The proposed adaptive template encodes prior geometric information of objects based on statistics of the training images. Given an input image, we utilize the adaptive template on the detected bounding boxes to initialize the raw labeling and OBF estimation...
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