Q
Qing-Yuan Jiang
Researcher at Nanjing University
Publications - 20
Citations - 1648
Qing-Yuan Jiang is an academic researcher from Nanjing University. The author has contributed to research in topics: Hash function & Nearest neighbor search. The author has an hindex of 8, co-authored 17 publications receiving 1136 citations.
Papers
More filters
Proceedings ArticleDOI
Deep Cross-Modal Hashing
Qing-Yuan Jiang,Wu-Jun Li +1 more
TL;DR: Deep cross-modal hashing (DCMH) as mentioned in this paper is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch.
Posted Content
Deep Cross-Modal Hashing
Qing-Yuan Jiang,Wu-Jun Li +1 more
TL;DR: DCMH is an end-to-end learning framework with deep neural networks, one for each modality, to perform feature learning from scratch and can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications.
Proceedings Article
Scalable graph hashing with feature transformation
Qing-Yuan Jiang,Wu-Jun Li +1 more
TL;DR: A novel method, called scalable graph hashing with feature transformation (SGH), for large-scale graph hashing that can effectively approximate the whole graph without explicitly computing the similarity graph matrix, based on which a sequential learning method is proposed to learn the hash functions in a bitwise manner.
Proceedings Article
Asymmetric Deep Supervised Hashing
Qing-Yuan Jiang,Wu-Jun Li +1 more
TL;DR: ADSH as discussed by the authors treats the query points and database points in an asymmetric way, and learns a deep hash function only for query points, while the hash codes for database points are directly learned.
Posted Content
Asymmetric Deep Supervised Hashing
Qing-Yuan Jiang,Wu-Jun Li +1 more
TL;DR: ADSH as discussed by the authors treats the query points and database points in an asymmetric way, and learns a deep hash function only for query points, while the hash codes for database points are directly learned.