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ORCIDLu Jin 0001
Person information
- unicode name: 金露
- affiliation: Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing, China
Other persons with the same name
- Lu Jin — disambiguation page
- Lu C. Jin (aka: Lu Jin 0002) — University of Western Australia, Perth, WA, Australia
- Lu Jin 0003
![[0000-0001-6901-4580]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-6901-4580")
— Fraunhofer Institute for Applied Information Technology (FIT), Sankt Augustin,, Germany - Lu Jin 0004 — Beijing Jiaotong University, School of Electronic and Information Engineering, Beijing, China
- Lu Jin 0005 — University of Southern Queensland, Department of Mathematics and Computing, Toowoomba, QLD, Australia
- Lu Jin 0006 — Shenyang Jianzhu University, School of Civil Engineering, Shenyang, China
- Lu Jin 0007 — Dalian University of Technology, School of Automotive Engineering, China
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2020 – today
- 2024
[j10]Wei Tang, Liang Li, Xuejing Liu, Lu Jin, Jinhui Tang, Zechao Li:
Context Disentangling and Prototype Inheriting for Robust Visual Grounding. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3213-3229 (2024)- [j9]Zhengyun Lu, Lu Jin, Zechao Li, Jinhui Tang:
Self-Paced Relational Contrastive Hashing for Large-Scale Image Retrieval. IEEE Trans. Multim. 26: 3392-3404 (2024) - [j8]Yonghua Pan, Jing Liu, Lu Jin, Zechao Li:
Unbiased Visual Question Answering by Leveraging Instrumental Variable. IEEE Trans. Multim. 26: 6648-6662 (2024) - 2023
- [j7]Lu Jin, Zechao Li, Jinhui Tang:
Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals. IEEE Trans. Neural Networks Learn. Syst. 34(4): 1838-1851 (2023) - [i3]Wei Tang, Liang Li, Xuejing Liu, Lu Jin, Jinhui Tang, Zechao Li:
Context Disentangling and Prototype Inheriting for Robust Visual Grounding. CoRR abs/2312.11967 (2023) - 2022
- [j6]Xinguang Xiang, Yajie Zhang, Lu Jin, Zechao Li, Jinhui Tang:
Sub-Region Localized Hashing for Fine-Grained Image Retrieval. IEEE Trans. Image Process. 31: 314-326 (2022) - 2021
- [c6]Yajie Zhang, Yuxuan Dai, Wei Tang, Lu Jin, Xinguang Xiang:
Self-Adaptive Hashing for Fine-Grained Image Retrieval. MMAsia 2021: 11:1-11:7 - 2020
- [c5]Lu Jin, Zechao Li, Yonghua Pan, Jinhui Tang:
Weakly-Supervised Image Hashing through Masked Visual-Semantic Graph-based Reasoning. ACM Multimedia 2020: 916-924
2010 – 2019
- 2019
- [j5]Qiuli Liu, Lu Jin, Zechao Li, Jinhui Tang:
Multimedia retrieval by deep hashing with multilevel similarity learning. J. Vis. Commun. Image Represent. 59: 150-158 (2019) - [j4]Lu Jin, Xiangbo Shu, Kai Li, Zechao Li, Guo-Jun Qi, Jinhui Tang:
Deep Ordinal Hashing With Spatial Attention. IEEE Trans. Image Process. 28(5): 2173-2186 (2019) - [j3]Lu Jin, Kai Li, Zechao Li, Fu Xiao, Guo-Jun Qi, Jinhui Tang:
Deep Semantic-Preserving Ordinal Hashing for Cross-Modal Similarity Search. IEEE Trans. Neural Networks Learn. Syst. 30(5): 1429-1440 (2019) - [c4]Xie Sun, Lu Jin, Zechao Li:
Attention-Aware Feature Pyramid Ordinal Hashing for Image Retrieval. MMAsia 2019: 5:1-5:6 - [i2]Lu Jin, Jinhui Tang, Zechao Li, Guo-Jun Qi, Fu Xiao:
Deep Semantic Multimodal Hashing Network for Scalable Multimedia Retrieval. CoRR abs/1901.02662 (2019) - 2018
- [j2]Lu Jin, Kai Li, Hao Hu, Guo-Jun Qi, Jinhui Tang:
Semantic Neighbor Graph Hashing for Multimodal Retrieval. IEEE Trans. Image Process. 27(3): 1405-1417 (2018) - [c3]Lu Jin, Qiuli Liu, Zechao Li:
Deep hashing with multilevel similarity learning for multimedia similarity search. ICIMCS 2018: 3:1-3:5 - [i1]Lu Jin, Xiangbo Shu, Kai Li, Zechao Li, Guo-Jun Qi, Jinhui Tang:
Deep Ordinal Hashing with Spatial Attention. CoRR abs/1805.02459 (2018) - 2015
- [j1]Jinhui Tang, Lu Jin, Zechao Li, Shenghua Gao:
RGB-D Object Recognition via Incorporating Latent Data Structure and Prior Knowledge. IEEE Trans. Multim. 17(11): 1899-1908 (2015) - [c2]Lu Jin, Zechao Li, Xiangbo Shu, Shenghua Gao, Jinhui Tang:
Partially Common-Semantic Pursuit for RGB-D Object Recognition. ACM Multimedia 2015: 959-962 - 2014
- [c1]Lu Jin, Shenghua Gao, Zechao Li, Jinhui Tang:
Hand-Crafted Features or Machine Learnt Features? Together They Improve RGB-D Object Recognition. ISM 2014: 311-319
Coauthor Index
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