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Yanqing Guo
Researcher at Dalian University of Technology
Publications - 39
Citations - 457
Yanqing Guo is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Steganalysis & JPEG. The author has an hindex of 11, co-authored 37 publications receiving 258 citations.
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Journal ArticleDOI
A Survey of Deep Facial Attribute Analysis
TL;DR: Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years as discussed by the authors, and a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation is provided.
Journal ArticleDOI
Multiple Robustness Enhancements for Image Adaptive Steganography in Lossy Channels
TL;DR: A series of experimental results demonstrate that the proposed algorithm can extract embedded messages with significantly higher accuracy after different attacks, compared with the state-of-the-art adaptive steganography, and robust watermarking algorithms, while maintaining good detection resistant performance.
Proceedings ArticleDOI
Partial Multi-view Subspace Clustering
TL;DR: This work proposes a novel multi-view clustering method, called Partial Multi-view Subspace Clustering (PMSC), that seeks the latent space and performs data reconstruction simultaneously to learn the subspace representation, leading to a more comprehensive data description.
Journal ArticleDOI
Synthesis linear classifier based analysis dictionary learning for pattern classification
TL;DR: This paper incorporates a synthesis-linear-classifier-based error term into the basic analysis dictionary learning model, whose classification performance is obviously improved by making full use of the label information, and develops an alternating iterative algorithm to solve the new model and obtain closed-form solutions leading to pretty competitive running efficiency.
Journal ArticleDOI
Class-Aware Analysis Dictionary Learning for Pattern Classification
TL;DR: This letter proposes a Class-aware Analysis Dictionary Learning (CADL) model to improve the classification performance of conventional ADL and demonstrates the superiority of the CADL method to the state-of-the-art DL methods.