135 citations · 161 across the 7 of their papers we have counts for
7 papers
Text-to-CAD Retrieval: a Strong Baseline
Honghu Pan, Zibo Du, Daxiang Liu +2
Text-based retrieval of Computer-Aided Design (CAD) models is a critical yet underexplored task for the reuse of legacy industrial designs. Existing CAD repositories are typically…
Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading
Haoran Li, Yuxin Lin, Huan Wang +9
Diabetic retinopathy (DR) is one of the leading causes of vision loss worldwide, making early and accurate DR grading critical for timely intervention. Recent clinical practices le…
Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection
Yongting Hu, Yuxin Lin, Chengliang Liu +4
Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still c…
Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
Chengliang Liu, Jie Wen, Yabo Liu +4
Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is stil…
Information Recovery-Driven Deep Incomplete Multiview Clustering Network
Chengliang Liu, Jie Wen, Zhihao Wu +3
Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To…
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
Chengliang Liu, Jie Wen, Xiaoling Luo +3
In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the…
Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers
Chengliang Liu, Jie Wen, Xiaoling Luo +1
As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view…