12 citations · 25 across the 20 of their papers we have counts for
4 papers · 1 filter
PPOM: Marginalizing Patch-Grid Phase for CLIP-Based Generalizable Vision-Language Prompt Tuning
Liang Wang, Haoyang Li, Chao Wang +3
Prompt tuning adapts CLIP-based vision-language models with few trainable parameters, yet its predictions remain sensitive to the spatial sampling imposed by a frozen vision transf…
Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
Shengchao Chen, Guodong Long, Dikai Liu +1
Heterogeneity in time series data is more pronounced than in vision or language, as temporal dynamics vary substantially across domains and tasks. Existing efforts on training time…
Discrete Prototypical Memories for Federated Time Series Foundation Models
Liwei Deng, Qingxiang Liu, Xinhe Niu +5
Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to…
FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models
Haoyang Li, Liang Wang, Siyu Zhou +5
CLIP-based prompt tuning enables pretrained Vision-Language Models (VLMs) to efficiently adapt to downstream tasks. Although existing studies have made significant progress, they p…