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Xing Xu
Researcher at University of Electronic Science and Technology of China
Publications - 172
Citations - 5513
Xing Xu is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Computer science & Image retrieval. The author has an hindex of 28, co-authored 133 publications receiving 3278 citations. Previous affiliations of Xing Xu include Kyushu University & Guizhou University.
Papers
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Proceedings ArticleDOI
Adversarial Cross-Modal Retrieval
TL;DR: Comprehensive experimental results show that the proposed ACMR method is superior in learning effective subspace representation and that it significantly outperforms the state-of-the-art cross-modal retrieval methods.
Journal ArticleDOI
Video Captioning With Attention-Based LSTM and Semantic Consistency
TL;DR: A novel end-to-end framework named aLSTMs, an attention-based LSTM model with semantic consistency, to transfer videos to natural sentences with competitive or even better results than the state-of-the-art baselines for video captioning in both BLEU and METEOR.
Journal ArticleDOI
Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval
TL;DR: A novel cross- modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints, and an effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes.
Journal ArticleDOI
Deep Fuzzy Hashing Network for Efficient Image Retrieval
TL;DR: The proposed deep fuzzy hashing network (DFHN) method combines the fuzzy logic technique and the DNN to learn more effective binary codes, which can leverage fuzzy rules to model the uncertainties underlying the data.
Journal ArticleDOI
Wound intensity correction and segmentation with convolutional neural networks
Huimin Lu,Huimin Lu,Huimin Lu,Bin Li,Junwu Zhu,Yujie Li,Yujie Li,Yun Li,Xing Xu,Li He,Xin Li,Jianru Li,Seiichi Serikawa +12 more
TL;DR: A fast level set model‐based method for intensity inhomogeneity correction and a spectral properties‐based color correction method to overcome obstacles in the wound healing process.