Institution
Dalian University of Technology
Education•Dalian, China•
About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.
Papers published on a yearly basis
Papers
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766 citations
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01 Oct 2017TL;DR: Amulet is presented, a generic aggregating multi-level convolutional feature framework for salient object detection that provides accurate salient object labeling and performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.
Abstract: Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and fine details. Then it adaptively learns to combine these feature maps at each resolution and predict saliency maps with the combined features. Finally, the predicted results are efficiently fused to generate the final saliency map. In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework. By aggregating multi-level convolutional features in this efficient and flexible manner, the proposed saliency model provides accurate salient object labeling. Comprehensive experiments demonstrate that our method performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.
759 citations
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TL;DR: The definition of the degree of similarity between IFSs is introduced and several new similarity measures between I FSs are proposed and corresponding proofs are given.
751 citations
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TL;DR: The dislocation etching concept introduced here may be helpful in the fabrication of superhydrophobic surfaces on other polycrystalline substrates.
Abstract: A surface roughening method by simple chemical etching was developed for the fabrication of superhydrophobic surfaces on three polycrystalline metals, namely aluminum, copper, and zinc. The key to the etching technique was the use of a dislocation etchant that preferentially dissolves the dislocation sites in the grains. The etched metallic surfaces, when hydrophobized with fluoroalkylsilane, exhibited superhydrophobic properties with water contact angles of larger than 150°, as well as roll-off angles of less than 10° for 8-μL drops. Also, the dislocation etching concept introduced here may be helpful in the fabrication of superhydrophobic surfaces on other polycrystalline substrates.
749 citations
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University of Ljubljana1, University of Birmingham2, Czech Technical University in Prague3, Linköping University4, Austrian Institute of Technology5, Carnegie Mellon University6, Parthenope University of Naples7, University of Isfahan8, Autonomous University of Madrid9, University of Ottawa10, University of Oxford11, Hong Kong Baptist University12, Kyiv Polytechnic Institute13, Middle East Technical University14, Hacettepe University15, King Abdullah University of Science and Technology16, Pohang University of Science and Technology17, University of Nottingham18, University at Albany, SUNY19, Chinese Academy of Sciences20, Dalian University of Technology21, Xi'an Jiaotong University22, Indian Institute of Space Science and Technology23, Hong Kong University of Science and Technology24, ASELSAN25, Australian National University26, Commonwealth Scientific and Industrial Research Organisation27, University of Missouri28, University of Verona29, Universidade Federal de Itajubá30, United States Naval Research Laboratory31, Marquette University32, Graz University of Technology33, Naver Corporation34, Imperial College London35, Electronics and Telecommunications Research Institute36, Zhejiang University37, University of Surrey38, Harbin Institute of Technology39, Lehigh University40
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
Abstract: The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).
744 citations
Authors
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Name | H-index | Papers | Citations |
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Yang Yang | 171 | 2644 | 153049 |
Yury Gogotsi | 171 | 956 | 144520 |
Hui Li | 135 | 2982 | 105903 |
Michael I. Posner | 134 | 414 | 104201 |
Anders Hagfeldt | 129 | 600 | 79912 |
Jian Zhou | 128 | 3007 | 91402 |
Chao Zhang | 127 | 3119 | 84711 |
Bin Wang | 126 | 2226 | 74364 |
Chi Lin | 125 | 1313 | 102710 |
Tao Zhang | 123 | 2772 | 83866 |
Bo Wang | 119 | 2905 | 84863 |
Zhenyu Zhang | 118 | 1167 | 64887 |
Liang Cheng | 116 | 1779 | 65520 |
Anthony G. Fane | 112 | 565 | 40904 |
Xuelong Li | 110 | 1044 | 46648 |