150 citations · 382 across the 30 of their papers we have counts for
11 papers · 1 filter
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training
Cheng Tan, Jingxuan Wei, Zhangyang Gao +5
Multimodal reasoning is a challenging task that requires models to reason across multiple modalities to answer questions. Existing approaches have made progress by incorporating la…
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information
Qun Zheng, Xihong Yang, Siwei Wang +2
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. M…
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Yi Wen, Suyuan Liu, Xinhang Wan +5
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information acr…
Reinforcement Graph Clustering with Unknown Cluster Number
Yue Liu, Ke Liang, Jun Xia +5
Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks in an unsupervised manner, has attracted great attention in recent years. Although the pe…
DealMVC: Dual Contrastive Calibration for Multi-view Clustering
Xihong Yang, Jiaqi Jin, Siwei Wang +7
Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the…
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
Xihong Yang, Cheng Tan, Yue Liu +7
Contrastive graph node clustering via learnable data augmentation is a hot research spot in the field of unsupervised graph learning. The existing methods learn the sampling distri…
Dink-Net: Neural Clustering on Large Graphs
Yue Liu, Ke Liang, Jun Xia +4
Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with deep neural networks, has achieved promising progress in recent years. However, the exis…
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning
Lingyuan Meng, Ke Liang, Bin Xiao +5
Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer long-tail data-poor relations, which has drawn increasing attention these years due to its practicalities. Th…
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
Meng Liu, Ke Liang, Yawei Zhao +5
Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast…
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou +7
Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample m…
Cluster-guided Contrastive Graph Clustering Network
Xihong Yang, Yue Liu, Sihang Zhou +6
Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recentl…