7 citations · 11 across the 5 of their papers we have counts for
6 papers
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
Yang Shen, Xuhao Sun, Xiu-Shen Wei +2
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-g…
ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval
Quan Cui, Qing-Yuan Jiang, Xiu-Shen Wei +2
Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional…
Deep Multi-Index Hashing for Person Re-Identification
Ming-Wei Li, Qing-Yuan Jiang, Wu-Jun Li
Traditional person re-identification (ReID) methods typically represent person images as real-valued features, which makes ReID inefficient when the gallery set is extremely large.…
Asymmetric Deep Supervised Hashing
Qing-Yuan Jiang, Wu-Jun Li
Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing ca…
Discrete Latent Factor Model for Cross-Modal Hashing
Qing-Yuan Jiang, Wu-Jun Li
Due to its storage and retrieval efficiency, cross-modal hashing~(CMH) has been widely used for cross-modal similarity search in multimedia applications. According to the training…
Deep Cross-Modal Hashing
Qing-Yuan Jiang, Wu-Jun Li
Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications. However, almost all exi…