This document summarizes research on using computer vision and machine learning techniques to perform object detection without full bounding box annotations. It describes a new multiple instance learning (MIL) method called CR-MILBOOST that can learn from weakly labeled image data where only the image label is provided, not exact object locations. It also discusses adapting pre-trained deep convolutional neural networks from classification to detection by fine-tuning layers on detection data. Experimental results show these methods can dramatically improve object detection performance compared to fully supervised training even when annotations are weak.