A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNN) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2M+ labeled classification images. Unfortunately, only a small fraction of those labels are available for the detection task. It is much cheaper and easier to collect large quantities of image-level labels from search engines than it is to collect detection data and label it with precise bounding boxes. In this paper, we propose a Deep Detection Adaptation (DDA) algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors. Our method has the potential to enable detection for the tens of thousands of categories that lack bounding box annotations, yet have plenty of classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge demonstrates the efficacy of our approach. This algorithm enables us to produce a >7.5K detector corresponding to available classification data from all leaf nodes in the ImageNet tree. Models and software are available at lsda.berkeleyvision.org.
Hoffman et al. REF propose a Large Scale Detection through Adaptation (LSDA) algorithm that learns the difference between the CNN parameters of the image classifier and object detector of a "fully labeled" category, and transfers this knowledge to CNN classifiers for categories without bounding box annotated data, turning them into detectors.
LSDA: Large Scale Detection Through Adaptation
Neural Information Processing Systems (NIPS) 2014
Computer Science