most citedMulti-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval

3 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20221 cited

Granular-Ball Fuzzy Set and Its Implementation in SVM

Shuyin Xia, Xiaoyu Lian, Guoyin Wang +2

Most existing fuzzy set methods use points as their input, which is the finest granularity from the perspective of granular computing. Consequently, these methods are neither effic…

cs.CV2022

One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and Pixel-Level Imbalance Learning

Dawei Dai, Chunjie Wang, Shuyin Xia +2

Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene. In conventional model…

cs.CV20223 cited

Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval

Dawei Dai, Xiaoyu Tang, Shuyin Xia +3

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo in a given query sketch. However, its widespread applicability is limited…

cs.LG20221 cited

An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation

Shuyin Xia, Xinyu Bai, Guoyin Wang +4

This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time. Rough set has…

cs.AI2020

LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection

Shuyin Xia, Wenhua Li, Guoyin Wang +3

In this paper, we propose and prove the theorem regarding the stability of attributes in a decision system. Based on the theorem, we propose the LRA framework for accelerating roug…

cs.LG2020

Ball k-means

Shuyin Xia, Daowan Peng, Deyu Meng +4

This paper presents a novel accelerated exact k-means algorithm called the Ball k-means algorithm, which uses a ball to describe a cluster, focusing on reducing the point-centroid…