VIPAMIN is a lightweight visual prompt initialization method that enhances self-supervised model adaptation by aligning prompts with semantic regions and expanding representational diversity.

We present a prompt based multimodal semantic segmentation on the basis of pertained single-modality RGB model

We introduce CaPT, leveraging both instance-aware and task-aware information for effective and efficient prompt-based learning.

We provide a scalable bandit architecture for prompt tuning of decision transformers for increased downstream performance.