J
Jing Liu
Researcher at Chinese Academy of Sciences
Publications - 694
Citations - 11190
Jing Liu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 35, co-authored 205 publications receiving 6671 citations. Previous affiliations of Jing Liu include Nanjing University & University of Wisconsin-Madison.
Papers
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Proceedings ArticleDOI
Dual Attention Network for Scene Segmentation
TL;DR: New state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset is achieved without using coarse data.
Proceedings Article
Unsupervised feature selection using nonnegative spectral analysis
TL;DR: A new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), which exploits the discriminative information and feature correlation simultaneously to select a better feature subset.
Journal ArticleDOI
Robust Structured Subspace Learning for Data Representation
TL;DR: A novel Robust Structured Subspace Learning (RSSL) algorithm by integrating image understanding and feature learning into a joint learning framework is proposed, and the learned subspace is adopted as an intermediate space to reduce the semantic gap between the low-level visual features and the high-level semantics.
Journal ArticleDOI
Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection
TL;DR: A novel unsupervised feature selection algorithm, named clustering-guided sparse structural learning (CGSSL), is proposed by integrating cluster analysis and sparse structural analysis into a joint framework and experimentally evaluated and demonstrated efficiency and effectiveness.
Journal ArticleDOI
Image annotation via graph learning
TL;DR: A Nearest Spanning Chain (NSC) method is proposed to construct the image-based graph, whose edge-weights are derived from the chain-wise statistical information instead of the traditional pairwise similarities.