Institution
Zhejiang Gongshang University
Education•Hangzhou, China•
About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.
Topics: Computer science, Chemistry, Adsorption, Catalysis, China
Papers published on a yearly basis
Papers
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TL;DR: It was the larger specific surface area rather than better crystallinity dominated the synergistic degradation dynamics under visible light irradiation with lower pH (2), greater catalyst loading amount (2g/L), proper RhB/Cr(VI) ratios (1:8) and higher light intensity (500 W).
103 citations
TL;DR: In this paper, a 3D Bi2O3-BiOI composite was constructed by in situ etching and exchanging of a BiO3 layer by I− in KI aqueous solution.
103 citations
TL;DR: Results showed that S. gregaria and A. mellifera have a potential for future applications for food, feed, or insect-based dietary supplements and protein-enriched fractions obtained from honey bee brood showed significantly higher protein heat coagulation than grasshopper and whey proteins.
102 citations
TL;DR: In this paper, the authors examine the effects of electronic commerce (e-commerce) channel entry on the profitability and behavior of manufacturers and physical retailers within a distribution system and show that if e-channel efficiency is adequately low or the acceptance of the conventional channel is low, the e-commerce channel may dominate the distribution system; otherwise, the conventional channels may dominate.
102 citations
15 Oct 2019
TL;DR: With W2VV++, a super version of Word2VisualVec previously developed for visual-to-text matching, a new baseline for ad-hoc video search is established, which outperforms the state-of-the-art.
Abstract: Ad-hoc video search (AVS) is an important yet challenging problem in multimedia retrieval. Different from previous concept-based methods, we propose a fully deep learning method for query representation learning. The proposed method requires no explicit concept modeling, matching and selection. The backbone of our method is the proposed W2VV++ model, a super version of Word2VisualVec (W2VV) previously developed for visual-to-text matching. W2VV++ is obtained by tweaking W2VV with a better sentence encoding strategy and an improved triplet ranking loss. With these simple yet important changes, W2VV++ brings in a substantial improvement. As our participation in the TRECVID 2018 AVS task and retrospective experiments on the TRECVID 2016 and 2017 data show, our best single model, with an overall inferred average precision (infAP) of 0.157, outperforms the state-of-the-art. The performance can be further boosted by model ensemble using late average fusion, reaching a higher infAP of 0.163. With W2VV++, we establish a new baseline for ad-hoc video search.
101 citations
Authors
Showing all 8318 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| David Julian McClements | 131 | 1137 | 71123 |
| Sajal K. Das | 85 | 1124 | 29785 |
| Ye Wang | 85 | 466 | 24052 |
| Xun Wang | 84 | 606 | 32187 |
| Tao Jiang | 82 | 940 | 27018 |
| Yueming Jiang | 79 | 452 | 20563 |
| Mo Wang | 61 | 274 | 13664 |
| Robert J. Linhardt | 58 | 1190 | 53368 |
| Jiankun Hu | 57 | 493 | 11430 |
| Xuming Zhang | 56 | 384 | 10788 |
| Yuan Li | 50 | 352 | 8771 |
| Chunping Yang | 49 | 173 | 8604 |
| Duo Li | 48 | 329 | 9060 |
| Matthew Campbell | 48 | 236 | 13448 |
| Aiqian Ye | 48 | 163 | 6120 |