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