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.
翻译:我们从考虑说明预算限制的新角度研究物体探测问题,适当创建预算意识物体探测(BAOD),在提供固定预算时,我们提出建立多样化和资料丰富的数据集的战略,以便最佳地培训强力探测器;我们调查优化和基于学习的方法,以抽样说明哪些图象,用哪些图象(大力或薄弱监督)作注释;我们采用混合监督的学习框架,从这两种注解中培训物体探测器;我们开展一项全面的经验性研究,表明手工制作的优化方法优于其他选择技术,包括随机抽样、不确定性取样和积极学习;通过将最佳的图像/说明选择办法与混合监督的学习相结合,解决巴沙德问题,我们表明,在2007年PACAL-VoC上,可以实现高度监督的注解器的性能,同时节省最初注注预算的12.8%;此外,在使用100美元的预算时,它比这一绩效高出2.0 mAP百分点。