  Active learning presents a promising avenue for training high-performance
models with minimal labeled data, achieved by judiciously selecting the most
informative instances to label and incorporating them into the task learner.
Despite notable advancements in active learning for image recognition, metrics
devised or learned to gauge the information gain of data, crucial for query
strategy design, do not consistently align with task model performance metrics,
such as Mean Average Precision (MeanAP) in object detection tasks. This paper
introduces MeanAP-Guided Reinforced Active Learning for Object Detection
(MAGRAL), a novel approach that directly utilizes the MeanAP metric of the task
model to devise a sampling strategy employing a reinforcement learning-based
sampling agent. Built upon LSTM architecture, the agent efficiently explores
and selects subsequent training instances, and optimizes the process through
policy gradient with MeanAP serving as reward. Recognizing the time-intensive
nature of MeanAP computation at each step, we propose fast look-up tables to
expedite agent training. We assess MAGRAL's efficacy across popular benchmarks,
PASCAL VOC and MS COCO, utilizing different backbone architectures. Empirical
findings substantiate MAGRAL's superiority over recent state-of-the-art
methods, showcasing substantial performance gains. MAGRAL establishes a robust
baseline for reinforced active object detection, signifying its potential in
advancing the field.
