  Test-time adaptation (TTA) addresses distribution shifts for streaming test
data in unsupervised settings. Currently, most TTA methods can only deal with
minor shifts and rely heavily on heuristic and empirical studies.
  To advance TTA under domain shifts, we propose the novel problem setting of
active test-time adaptation (ATTA) that integrates active learning within the
fully TTA setting.
  We provide a learning theory analysis, demonstrating that incorporating
limited labeled test instances enhances overall performances across test
domains with a theoretical guarantee. We also present a sample entropy
balancing for implementing ATTA while avoiding catastrophic forgetting (CF). We
introduce a simple yet effective ATTA algorithm, known as SimATTA, using
real-time sample selection techniques. Extensive experimental results confirm
consistency with our theoretical analyses and show that the proposed ATTA
method yields substantial performance improvements over TTA methods while
maintaining efficiency and shares similar effectiveness to the more demanding
active domain adaptation (ADA) methods. Our code is available at
https://github.com/divelab/ATTA
