Greening at multiple scales promote biodiverse cities: A multi-scale assessment of drivers of Neotropical birds

Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages, but also non-adjacent stages. To fuse these features, we design a simple and effective dense connection module (DCM). In addition, we offer a new consideration of the common upscale operation in our approach. We believe DCPNet offers a more efficient way to fuse features from multiple scales in a pyramid-like network. We perform extensive experiments using both outdoor and indoor benchmark datasets (i.e., the KITTI and the NYU Depth V2 datasets) and DCPNet achieves the state-of-the-art results.

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Multi-Scale Assessment of Prediction Uncertainty in Coupled Reactive Transport Models Conducted at the Florida State University

10.2172/1104708 ◽

2013 ◽

Author(s):

Ming Ye

Keyword(s):

Reactive Transport ◽

Florida State University ◽

State University ◽

Transport Models ◽

Prediction Uncertainty ◽

Multi Scale ◽

Scale Assessment

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Multi-scale assessment of human-induced changes to Amazonian instream habitats

Landscape Ecology ◽

10.1007/s10980-016-0358-x ◽

2016 ◽

Vol 31 (8) ◽

pp. 1725-1745 ◽

Cited By ~ 52

Author(s):

Cecília G. Leal ◽

Paulo S. Pompeu ◽

Toby A. Gardner ◽

Rafael P. Leitão ◽

Robert M. Hughes ◽

...

Keyword(s):

Multi Scale ◽

Scale Assessment ◽

Induced Changes

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Multi-Scale Assessment of Gas/Oil Displacements in a Thinly-Layered (Tidal) Reservoir System

61st EAGE Conference and Exhibition ◽

10.3997/2214-4609.201407662 ◽

1999 ◽

Author(s):

P. Ringrose ◽

A. Sharif ◽

P. Kristian Munkerud ◽

A. Næss

Keyword(s):

Gas Oil ◽

Multi Scale ◽

Reservoir System ◽

Scale Assessment

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A Novel Multi-Scale Feature Fusion Method for Region Proposal Network in Fast Object Detection

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2020070107 ◽

2020 ◽

Vol 16 (3) ◽

pp. 132-145

Author(s):

Gang Liu ◽

Chuyi Wang

Keyword(s):

Object Detection ◽

Multiple Scales ◽

Feature Fusion ◽

Uniform Space ◽

Fusion Method ◽

Well Performance ◽

Feature Maps ◽

Neural Network Models ◽

Scale Feature ◽

Multi Scale

Neural network models have been widely used in the field of object detecting. The region proposal methods are widely used in the current object detection networks and have achieved well performance. The common region proposal methods hunt the objects by generating thousands of the candidate boxes. Compared to other region proposal methods, the region proposal network (RPN) method improves the accuracy and detection speed with several hundred candidate boxes. However, since the feature maps contains insufficient information, the ability of RPN to detect and locate small-sized objects is poor. A novel multi-scale feature fusion method for region proposal network to solve the above problems is proposed in this article. The proposed method is called multi-scale region proposal network (MS-RPN) which can generate suitable feature maps for the region proposal network. In MS-RPN, the selected feature maps at multiple scales are fine turned respectively and compressed into a uniform space. The generated fusion feature maps are called refined fusion features (RFFs). RFFs incorporate abundant detail information and context information. And RFFs are sent to RPN to generate better region proposals. The proposed approach is evaluated on PASCAL VOC 2007 and MS COCO benchmark tasks. MS-RPN obtains significant improvements over the comparable state-of-the-art detection models.

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