The main features of AIRS can be summarized as: 457 km^2 coverage of orthorectified aerial images with over 226,342 labeled buildings; Very high spatial resolution of imagery (0.075m); Refined ground truths that strictly align with roof outlines; covers almost the full area of Christchurch, ...
Run-time analysis. Two properties make detection efficient. First, all CNN parameters are shared across all categories. Second, the feature vectors computed by the CNN are low-dimensional when compared to other common approaches, such as spatial pyramids with bag-of-visualword encodings. Th...
The integration of these components forms the backbone of HFA-Net, which we present as a new 3D semantic segmentation network. In summary, our main contributions can be summarized as follows: (1) We propose the Hybrid Feature Encoding Component, which fully utilizes the color information and ...
The remainder of this paper is organized as follows. We provides an overview of related work in cross-modal hashing methods in the first place. Then, our proposed method and training process are presented. Next, experimental results and corresponding analysis are presented. Finally, we summarize ...
Currently, segmentation is widely applied on many vision-based applications such as medical image analysis [1], autonomous driving [2] and remote sensing [3], [4]. Some traditional methods [5], [6], [7] on this task exploit feature manually. Recent CNN-based approaches for semantic ...
(70 min of unique audio stimulus in total) while their brain signal is recorded using functional MRI. At the end of each story, a questionnaire is submitted to each subject to assess their understanding, and the answers are summarized into a comprehension score specific to each (narrative, ...
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Run-time analysis. Two properties make detection efficient. First, all CNN parameters are shared across all categories. Second, the feature vectors computed by the CNN are low-dimensional when compared to other common approaches, such as spatial pyramids with bag-of-visual-word encodings. The feat...
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and the gist of which can be summarized as follows: (1) that LSA recovers latent semantic factors underlying the document space, (2) that such can be accomplished through lossy compression of the document space by eliminating lexical noise, and (3) that the latter can best be achieved by ...