5 papers
MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios
Guo Li, Jiandian Zeng, Yang Li +3
Deploying Video Anomaly Detection (VAD) in real-world surveillance faces a fundamental tension between the demand for high-level semantics to ensure effectiveness and the limited c…
HiLoRA: Hierarchical Low-Rank Adaptation for Personalized Federated Learning
Zihao Peng, Nan Zou, Jiandian Zeng +4
Vision Transformers (ViTs) have been widely adopted in vision tasks due to their strong transferability. In Federated Learning (FL), where full fine-tuning is communication heavy,…
Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development
Changfu Xu, Jianxiong Guo, Yuzhu Liang +7
Diffusion Models (DMs), as a leading class of generative models, offer key advantages for reinforcement learning (RL), including multi-modal expressiveness, stable training, and tr…
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
Zihao Peng, Jiandian Zeng, Boyuan Li +3
Federated Learning (FL) facilitates the fine-tuning of Foundation Models (FMs) using distributed data sources, with Low-Rank Adaptation (LoRA) gaining popularity due to its low com…
Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks
Changfu Xu, Jianxiong Guo, Wanyu Lin +5
Artificial Intelligence Generated Content (AIGC) has gained significant popularity for creating diverse content. Current AIGC models primarily focus on content quality within a cen…