no code implementations • 1 Mar 2024 • Ruoqi Wang, Haitao Wang, Qiong Luo, Feng Wang, Hejun Wu
This hybrid approach allows VisRec to effectively leverage both labeled and unlabeled data.
no code implementations • 21 Jan 2022 • Shangrong Yu, Yuxin Chen, Hejun Wu
Low-rank inductive matrix completion (IMC) is currently widely used in IoT data completion, recommendation systems, and so on, as the side information in IMC has demonstrated great potential in reducing sample point remains a major obstacle for the convergence of the nonconvex solutions to IMC.
no code implementations • 28 Aug 2021 • Ruoqi Wang, Ziwang Huang, Haitao Wang, Hejun Wu
Different from previous works, AMMASurv can effectively utilize the intrinsic information within every modality and flexibly adapts to the modalities of different importance.
no code implementations • 30 Oct 2018 • Guanbin Li, Yukang Gan, Hejun Wu, Nong Xiao, Liang Lin
In this paper, we address this problem by developing a Cross-Modal Attentional Context (CMAC) learning framework, which enables the full exploitation of the context information from both RGB and depth data.