BAOD: Budget-Aware Object Detection
This addresses the problem of reducing annotation costs for object detection tasks, which is an incremental improvement in efficiency for computer vision applications.
The paper tackles the problem of object detection under annotation budget constraints by proposing a strategy to build diverse and informative datasets, combining optimal image/annotation selection with hybrid supervised learning. It achieves the performance of a strongly supervised detector on PASCAL-VOC 2007 while saving 12.8% of the annotation budget and surpasses it by 2.0 mAP points when using the full budget.
We study the problem of object detection from a novel perspective in which annotation budget constraints are taken into consideration, appropriately coined Budget Aware Object Detection (BAOD). When provided with a fixed budget, we propose a strategy for building a diverse and informative dataset that can be used to optimally train a robust detector. We investigate both optimization and learning-based methods to sample which images to annotate and what type of annotation (strongly or weakly supervised) to annotate them with. We adopt a hybrid supervised learning framework to train the object detector from both these types of annotation. We conduct a comprehensive empirical study showing that a handcrafted optimization method outperforms other selection techniques including random sampling, uncertainty sampling and active learning. By combining an optimal image/annotation selection scheme with hybrid supervised learning to solve the BAOD problem, we show that one can achieve the performance of a strongly supervised detector on PASCAL-VOC 2007 while saving 12.8% of its original annotation budget. Furthermore, when $100\%$ of the budget is used, it surpasses this performance by 2.0 mAP percentage points.