Continual Test-Time Adaptation (TTA) seeks to adapt a source pre-trained
model to continually changing, unlabeled target domains. Existing TTA methods
are typically designed for environments where domain changes occur gradually
and can struggle in more dynamic scenarios. Inspired by the principles of
online K-Means, this paper introduces a novel approach to continual TTA through
visual prompting. We propose a Dynamic Prompt Coreset that not only preserves
knowledge from previously visited domains but also accommodates learning from
new potential domains. This is complemented by a distance-based weight updating
mechanism that ensures the coreset remains current and relevant. Our approach
employs a fixed model architecture alongside the coreset and an innovative
updating system to effectively mitigate challenges such as catastrophic
forgetting and error accumulation. Extensive testing across various
benchmarks-including ImageNet-C, CIFAR100-C, and CIFAR10-C-demonstrates that
our method consistently outperforms state-of-the-art (SOTA) alternatives,
particularly excelling in dynamically changing environments.

本文介绍了一种通过视觉提示的动态提示核心集方法来实现持续的测试时间自适应（TTA），该方法不仅保留了先前访问的领域的知识，而且适应了新潜在领域的学习。经过广泛的测试，包括 ImageNet-C，CIFAR100-C 和 CIFAR10-C 等基准，显示出我们的方法始终优于最先进的替代方法，在动态变化的环境中表现突出，特别是在遇到灾难性遗忘和误差累积等挑战时。

动态域，动态解决方案：用于连续测试时适应的 DPCore

Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time  Adaptation

The problem of clustering content in social media has pervasive applications,
including the identification of discussion topics, event detection, and content
recommendation. Here we describe a streaming framework for online detection and
clustering of memes in social media, specifically Twitter. A pre-clustering
procedure, namely protomeme detection, first isolates atomic tokens of
information carried by the tweets. Protomemes are thereafter aggregated, based
on multiple similarity measures, to obtain memes as cohesive groups of tweets
reflecting actual concepts or topics of discussion. The clustering algorithm
takes into account various dimensions of the data and metadata, including
natural language, the social network, and the patterns of information
diffusion. As a result, our system can build clusters of semantically,
structurally, and topically related tweets. The clustering process is based on
a variant of Online K-means that incorporates a memory mechanism, used to
"forget" old memes and replace them over time with the new ones. The evaluation
of our framework is carried out by using a dataset of Twitter trending topics.
Over a one-week period, we systematically determined whether our algorithm was
able to recover the trending hashtags. We show that the proposed method
outperforms baseline algorithms that only use content features, as well as a
state-of-the-art event detection method that assumes full knowledge of the
underlying follower network. We finally show that our online learning framework
is flexible, due to its independence of the adopted clustering algorithm, and
best suited to work in a streaming scenario.

该论文介绍了一种针对 Twitter 的流式框架，用于在线检测和聚类社交媒体中的模因，通过 “原型模因检测” 预聚类后，在多个相似性衡量标准的基础上聚合原型模因，建立起与语义、结构、主题数相关的推文集群。