1,976 research outputs found
On-line monitoring of relative dielectric losses in cross-bonded cables using sheath currents
Quality-Gated Convolutional LSTM for Enhancing Compressed Video
The past decade has witnessed great success in applying deep learning to
enhance the quality of compressed video. However, the existing approaches aim
at quality enhancement on a single frame, or only using fixed neighboring
frames. Thus they fail to take full advantage of the inter-frame correlation in
the video. This paper proposes the Quality-Gated Convolutional Long Short-Term
Memory (QG-ConvLSTM) network with bi-directional recurrent structure to fully
exploit the advantageous information in a large range of frames. More
importantly, due to the obvious quality fluctuation among compressed frames,
higher quality frames can provide more useful information for other frames to
enhance quality. Therefore, we propose learning the "forget" and "input" gates
in the ConvLSTM cell from quality-related features. As such, the frames with
various quality contribute to the memory in ConvLSTM with different importance,
making the information of each frame reasonably and adequately used. Finally,
the experiments validate the effectiveness of our QG-ConvLSTM approach in
advancing the state-of-the-art quality enhancement of compressed video, and the
ablation study shows that our QG-ConvLSTM approach is learnt to make a
trade-off between quality and correlation when leveraging multi-frame
information. The project page: https://github.com/ryangchn/QG-ConvLSTM.git.Comment: Accepted to IEEE International Conference on Multimedia and Expo
(ICME) 201
An Optimization-based Matching Method and its Application in Merging Administrative Boundary Data
This presentation was given as part of the GIS Day@KU symposium on November 13, 2019. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2019/As a critical data management task, conflation in GIS aims to determine the corresponding features from different datasets that in reality represent the same entities. This is called feature matching, which is used as a guidance to merge attributes of corresponding features between datasets. Based on the classification of features, there are point, polyline, and polygon matching methods. This study focuses on matching polygons and explores optimization–based matching methods for conflating two datasets.PLATINUM SPONSORS:
KU Department of Geography and Atmospheric Science
KU Institute for Policy & Social Research
GOLD SPONSORS:
KU Libraries
State of Kansas Data Access & Support Center (DASC)
SILVER SPONSORS:
Bartlett & West
Kansas Applied Remote Sensing Program
KU Center for Global and International Studies
BRONZE SPONSORS:
Boundles
Target-Tailored Source-Transformation for Scene Graph Generation
Scene graph generation aims to provide a semantic and structural description
of an image, denoting the objects (with nodes) and their relationships (with
edges). The best performing works to date are based on exploiting the context
surrounding objects or relations,e.g., by passing information among objects. In
these approaches, to transform the representation of source objects is a
critical process for extracting information for the use by target objects. In
this work, we argue that a source object should give what tar-get object needs
and give different objects different information rather than contributing
common information to all targets. To achieve this goal, we propose a
Target-TailoredSource-Transformation (TTST) method to efficiently propagate
information among object proposals and relations. Particularly, for a source
object proposal which will contribute information to other target objects, we
transform the source object feature to the target object feature domain by
simultaneously taking both the source and target into account. We further
explore more powerful representations by integrating language prior with the
visual context in the transformation for the scene graph generation. By doing
so the target object is able to extract target-specific information from the
source object and source relation accordingly to refine its representation. Our
framework is validated on the Visual Genome bench-mark and demonstrated its
state-of-the-art performance for the scene graph generation. The experimental
results show that the performance of object detection and visual relation-ship
detection are promoted mutually by our method
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