关键词：对象检测；对象识别；机器学习；简约法
摘 要：In this thesis, I present two instantiations of model parsimony for large scale object detection and discovery. For model inference, I present sparselet models which significantly reduce model inference complexity by utilizing a shared representation, reconstruction sparsity, and parallelism to enable real-time multiclass object detection with deformable part models at 5Hz with almost no decrease in task performance. For model learning, I present a framework for training object detectors using only one-bit image level annotations of object presence without any instance level annotations (i.e. bounding boxes).