DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation
Yunbei Zhang, Akshay Mehra, Shuaicheng Niu, Jihun Hamm

TL;DR
DPCore introduces a robust, efficient method for continual test-time adaptation that dynamically manages domain shifts by intelligently updating prompts, significantly improving performance and reducing computational costs.
Contribution
The paper presents DPCore, a novel approach that dynamically adjusts prompts for continual test-time adaptation, addressing domain recurrence and variability issues.
Findings
Outperforms existing CTTA methods across four benchmarks.
Achieves state-of-the-art results in both structured and dynamic domain settings.
Reduces trainable parameters by 99% and computation time by 64%.
Abstract
Continual Test-Time Adaptation (CTTA) seeks to adapt source pre-trained models to continually changing, unseen target domains. While existing CTTA methods assume structured domain changes with uniform durations, real-world environments often exhibit dynamic patterns where domains recur with varying frequencies and durations. Current approaches, which adapt the same parameters across different domains, struggle in such dynamic conditions-they face convergence issues with brief domain exposures, risk forgetting previously learned knowledge, or misapplying it to irrelevant domains. To remedy this, we propose DPCore, a method designed for robust performance across diverse domain change patterns while ensuring computational efficiency. DPCore integrates three key components: Visual Prompt Adaptation for efficient domain alignment, a Prompt Coreset for knowledge preservation, and a Dynamic…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
Taxonomy
TopicsFault Detection and Control Systems
