  Active learning selects the most informative samples to exploit limited
annotation budgets. Existing work follows a cumbersome pipeline that repeats
the time-consuming model training and batch data selection multiple times. In
this paper, we challenge this status quo by proposing a novel general and
efficient active learning (GEAL) method following our designed new pipeline.
Utilizing a publicly available pretrained model, our method selects data from
different datasets with a single-pass inference of the same model without extra
training or supervision. To capture subtle local information, we propose
knowledge clusters extracted from intermediate features. Free from the
troublesome batch selection strategy, all data samples are selected in one-shot
through a distance-based sampling in the fine-grained knowledge cluster level.
This whole process is faster than prior arts by hundreds of times. Extensive
experiments verify the effectiveness of our method on object detection, image
classification, and semantic segmentation. Our code is publicly available in
https://github.com/yichen928/GEAL_active_learning.
