no code implementations • 18 Dec 2024 • Wenchao Xu, Jinyu Chen, Peirong Zheng, Xiaoquan Yi, Tianyi Tian, Wenhui Zhu, Quan Wan, Haozhao Wang, Yunfeng Fan, Qinliang Su, Xuemin Shen
Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward Artificial General Intelligence (AGI).
1 code implementation • 28 Jul 2024 • Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junhong Liu, Song Guo
Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data.
no code implementations • 31 Dec 2023 • Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu, Song Guo
Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data.
1 code implementation • 31 Dec 2023 • Yunfeng Fan, Wenchao Xu, Haozhao Wang, Fushuo Huo, Jinyu Chen, Song Guo
On the other hand, we propose the modality selection aiming to select subsets of local modalities with great diversity and achieving global modal balance simultaneously.
no code implementations • 20 Mar 2023 • Fushuo Huo, Wenchao Xu, Jingcai Guo, Haozhao Wang, Yunfeng Fan, Song Guo
In this paper, we propose a novel Dual-prototype Self-augment and Refinement method (DSR) for NO-CL problem, which consists of two strategies: 1) Dual class prototypes: vanilla and high-dimensional prototypes are exploited to utilize the pre-trained information and obtain robust quasi-orthogonal representations rather than example buffers for both privacy preservation and memory reduction.
no code implementations • 14 Mar 2023 • Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu, Junxiao Wang, Song Guo
Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones.
no code implementations • CVPR 2023 • Yunfeng Fan, Wenchao Xu, Haozhao Wang, Junxiao Wang, Song Guo
Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to compensate for their inherent limitations.