10 citations · 14 across the 6 of their papers we have counts for
6 papers
DiNAT-IR: Exploring Dilated Neighborhood Attention for High-Quality Image Restoration
Hanzhou Liu, Binghan Li, Chengkai Liu +1
Transformers, with their self-attention mechanisms for modeling long-range dependencies, have become a dominant paradigm in image restoration tasks. However, the high computational…
XYScanNet: A State Space Model for Single Image Deblurring
Hanzhou Liu, Chengkai Liu, Jiacong Xu +2
Deep state-space models (SSMs), like recent Mamba architectures, are emerging as a promising alternative to CNN and Transformer networks. Existing Mamba-based restoration methods p…
TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering
Chengkai Liu, Jianling Wang, James Caverlee
In the domain of recommendation and collaborative filtering, Graph Contrastive Learning (GCL) has become an influential approach. Nevertheless, the reasons for the effectiveness of…
Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation
Chengkai Liu, Jianghao Lin, Hanzhou Liu +2
Sequential recommender systems aims to predict the users' next interaction through user behavior modeling with various operators like RNNs and attentions. However, existing models…
DeblurDiNAT: A Compact Model with Exceptional Generalization and Visual Fidelity on Unseen Domains
Hanzhou Liu, Binghan Li, Chengkai Liu +1
Recent deblurring networks have effectively restored clear images from the blurred ones. However, they often struggle with generalization to unknown domains. Moreover, these models…
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
Chengkai Liu, Jianghao Lin, Jianling Wang +2
Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven t…