7 citations · 24 across the 16 of their papers we have counts for
6 papers · 1 filter
Deep Manifold Hashing: A Divide-and-Conquer Approach for Semi-Paired Unsupervised Cross-Modal Retrieval
Yufeng Shi, Xinge You, Jiamiao Xu +3
Hashing that projects data into binary codes has shown extraordinary talents in cross-modal retrieval due to its low storage usage and high query speed. Despite their empirical suc…
Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval
Yufeng Shi, Shujian Yu, Duanquan Xu +1
Zero-shot cross-modal retrieval (ZS-CMR) deals with the retrieval problem among heterogenous data from unseen classes. Typically, to guarantee generalization, the pre-defined class…
Leachable Component Clustering
Miao Cheng, Xinge You
Clustering attempts to partition data instances into several distinctive groups, while the similarities among data belonging to the common partition can be principally reserved. Fu…
Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis
Yufeng Shi, Shuhuang Chen, Xinge You +3
Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneo…
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning
Shiming Chen, Ziming Hong, Guo-Sen Xie +5
The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus achieving a desirable kn…
R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction
Yu Wang, Shuo Ye, Shujian Yu +1
Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories, whose main challenge is the large intraclass diversities and subtle inter-class differences. E…