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Found 7 papers, 2 papers with code
Deploying Foundation Model Powered Agent Services: A Survey
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).
Detached and Interactive Multimodal Learning
Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data.
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration
Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data.
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection
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.
Non-Exemplar Online Class-incremental Continual Learning via Dual-prototype Self-augment and Refinement
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.
DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning
Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones.
PMR: Prototypical Modal Rebalance for Multimodal Learning
Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to compensate for their inherent limitations.
