Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

In this paper, we introduce Attention Prompt Tuning (APT)-a computationally efficient variant
of prompt tuning for video-based applications such as action recognition. Prompt tuning …

Multi-modal transformers mark significant progress in different domains, but siloed high-
quality data hinders their further improvement. To remedy this, federated learning (FL) has …

Personalized federated learning aims to address data heterogeneity across local clients in
federated learning. However, current methods blindly incorporate either full model …

Federated learning is a distributed learning approach that balances data privacy and
collaborative learning. To address the impact of non-IID (non-Independently and Identically …

In this paper, we introduce Attention Prompt Tuning (APT)–a computationally efficient variant
of prompt tuning for video-based applications such as action recognition. Prompt tuning …