8,018 research outputs found
STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction
Human mobility forecasting in a city is of utmost importance to
transportation and public safety, but with the process of urbanization and the
generation of big data, intensive computing and determination of mobility
pattern have become challenging. This study focuses on how to improve the
accuracy and efficiency of predicting citywide human mobility via a simpler
solution. A spatio-temporal mobility event prediction framework based on a
single fully-convolutional residual network (STAR) is proposed. STAR is a
highly simple, general and effective method for learning a single tensor
representing the mobility event. Residual learning is utilized for training the
deep network to derive the detailed result for scenarios of citywide
prediction. Extensive benchmark evaluation results on real-world data
demonstrate that STAR outperforms state-of-the-art approaches in single- and
multi-step prediction while utilizing fewer parameters and achieving higher
efficiency.Comment: Accepted by MDM 201
Distance Guided Channel Weighting for Semantic Segmentation
Recent works have achieved great success in improving the performance of
multiple computer vision tasks by capturing features with a high channel number
utilizing deep neural networks. However, many channels of extracted features
are not discriminative and contain a lot of redundant information. In this
paper, we address above issue by introducing the Distance Guided Channel
Weighting (DGCW) Module. The DGCW module is constructed in a pixel-wise context
extraction manner, which enhances the discriminativeness of features by
weighting different channels of each pixel's feature vector when modeling its
relationship with other pixels. It can make full use of the high-discriminative
information while ignore the low-discriminative information containing in
feature maps, as well as capture the long-range dependencies. Furthermore, by
incorporating the DGCW module with a baseline segmentation network, we propose
the Distance Guided Channel Weighting Network (DGCWNet). We conduct extensive
experiments to demonstrate the effectiveness of DGCWNet. In particular, it
achieves 81.6% mIoU on Cityscapes with only fine annotated data for training,
and also gains satisfactory performance on another two semantic segmentation
datasets, i.e. Pascal Context and ADE20K. Code will be available soon at
https://github.com/LanyunZhu/DGCWNet
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