Resolving classification ambiguities in convolutional neural networks using hierarchical structures
We have recently witnessed the revolution of deep learning and convolutional neural networks enabled by the powerful machines available today. Convolutional neural networks have demonstrated excellent performance on various vision tasks, most importantly classification and detection. Nevertheless, there are some difficulties in the way of perfect performance. One problem is discriminating among objects that look extremely similar visually but semantically they are different. Another problem is the high cost of training large detection models. The same cost applies when the model is required to detect a new type of objects. In this work those problems are handled by introducing visual concepts and the use of hierarchical structures. We will see how the accuracy of classifying similar objects can be highly improved and how the time of accommodating for new objects in a detection model can be reduced from days to hours.
Rostock, Univ., Master Thesis, 2018