# An Adaptive Supervision Framework for Active Learning in Object Detection Source: [An Adaptive Supervision Framework for Active Learning in Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:59:47.071Z Authors: Sai Vikas Desai, Akshay L Chandra, Wei Guo, Seishi Ninomiya, Vineeth N Balasubramanian Published: 2019-08-07T06:20:33.000Z ## Abstract Active learning approaches in computer vision generally involve querying strong labels for data. However, previous works have shown that weak supervision can be effective in training models for vision tasks while greatly reducing annotation costs. Using this knowledge, we propose an adaptive supervision framework for active learning and demonstrate its effectiveness on the task of object detection. Instead of directly querying bounding box annotations (strong labels) for the most informative samples, we first query weak labels and optimize the model. Using a switching condition, the required supervision level can be increased. Our framework requires little to no change in model architecture. Our extensive experiments show that the proposed framework can be used to train good generalizable models with much lesser annotation costs than the state of the art active learning approaches for object detection. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()