Deep neural networks based object detectors have shown great success in a variety of domains like autonomous vehicles, biomedical imaging, etc. It is known that their success depends on a large amount of data from the domain of interest. While deep models often perform well in terms of overall accuracy, they often struggle in performance on rare yet critical data slices. For example, data slices like "motorcycle at night" or "bicycle at night" are often rare but very critical slices for self-driving applications and false negatives on such rare slices could result in ill-fated failures and accidents. Active learning (AL) is a well-known paradigm to incrementally and adaptively build training datasets with a human in the loop. However, current AL based acquisition functions are not well-equipped to tackle real-world datasets with rare slices, since they are based on uncertainty scores or global descriptors of the image. We propose TALISMAN, a novel framework for Targeted Active Learning or object detectIon with rare slices using Submodular MutuAl iNformation. Our method uses the submodular mutual information functions instantiated using features of the region of interest (RoI) to efficiently target and acquire data points with rare slices. We evaluate our framework on the standard PASCAL VOC07+12 and BDD100K, a real-world self-driving dataset. We observe that TALISMAN outperforms other methods by in terms of average precision on rare slices, and in terms of mAP.
翻译:以深心神经网络为基础的天体探测器在自主飞行器、生物医学成像等各个领域都表现出了巨大的成功。 众所周知, 其成功取决于来自感兴趣领域的大量数据。 虽然深心模型在总体准确性方面往往表现良好, 却往往在难得但关键的数据切片上难以运行。 例如, “ 夜间的摩托车” 或“ 夜间的双周期” 等数据切片通常很少见,但对于自我驱动应用来说却非常关键,而且这类稀有切片的假底片可能导致错误的失败和事故。 积极学习(AL)是一个众所周知的范例, 以渐进和适应的方式在循环中用人来建立培训数据集。 然而, 以 AL 为基础的获取功能往往无法很好地用稀有的切片处理真实世界数据集。 我们建议TALISMAN, 目标主动学习或目标探测器的新框架, 用稀有的精密切片片段, 我们的方法使用亚质的精度共同信息功能, 利用我们稀有的平面标准平面图,, 获得我们平面的平面的平面图。