English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Continual Test-Time Adaptation (CTTA) aims to adapt the source model tocontinually changing unlabeled target domains without access to the sourcedata. Existing methods mainly focus on model-based adaptation in aself-training manner, such as predicting pseudo labels for new domain datasets.Since pseudo labels are noisy and unreliable, these methods suffer fromcatastrophic forgetting and error accumulation when dealing with dynamic datadistributions. Motivated by the prompt learning in NLP, in this paper, wepropose to learn an image-level visual domain prompt for target domains whilehaving the source model parameters frozen. During testing, the changing targetdatasets can be adapted to the source model by reformulating the input datawith the learned visual prompts. Specifically, we devise two types of prompts,i.e., domains-specific prompts and domains-agnostic prompts, to extract currentdomain knowledge and maintain the domain-shared knowledge in the continualadaptation. Furthermore, we design a homeostasis-based prompt adaptationstrategy to suppress domain-sensitive parameters in domain-invariant prompts tolearn domain-shared knowledge more effectively. This transition from themodel-dependent paradigm to the model-free one enables us to bypass thecatastrophic forgetting and error accumulation problems. Experiments show thatour proposed method achieves significant performance gains overstate-of-the-art methods on four widely-used benchmarks, including CIFAR-10C,CIFAR-100C, ImageNet-C, and VLCS datasets.

Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation