Researchers at ICSI and UC Berkeley are developing new representation learning models for visual detection, leveraging advances in discriminatively trained convolutional neural networks. In 2013, they established important results related to these models, including observations of their ability to generalize to new tasks and domains, and importantly to be applicable to detection and segmentation tasks. They developed a new “Region-CNN” model (R-CNN), which outperformed all competing methods on the most important visual detection benchmark, the PASCAL challenge.