A Viewpoint Aware Multi-Task Learning Framework for Fine-Grained Vehicle Recognition

A Probabilisti Framework for Multi-Task Learning

Unsupervised Template Learning for Fine-Grained Object Recognition

Fine-grained recognition refers to a subordinate level of recognition, such as recognizing different species of animals and plants. It differs from recognition of basic categories, such as humans, tables, and computers, in that there are global similarities in shape and structure shared cross different categories, and the differences are in the details of object parts. We suggest that the key t...

Taxonomic Curriculum Learning for Fine-Grained Object Recognition

Drawing inspiration from taxonomic methods of learning and curriculum learning, we propose a new learning method: taxonomic curriculum learning. With this method, given a visual taxonomy of finegrained classes, a classifier is trained in a sequential coarse to fine manner. We compare our method with two baseline methods: the standard approach of purely fine-grained learning and multi-task learn...

Multi-frame Super Resolution for Improving Vehicle Licence Plate Recognition

License plate recognition (LPR) by digital image processing, which is widely used in traffic monitor and control, is one of the most important goals in Intelligent Transportation System (ITS). In real ITS, the resolution of input images are not very high since technology challenges and cost of high resolution cameras. However, when the license plate image is taken at low resolution, the license...

Fine-Grained Entity Recognition

Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g., person, organization, location or miscellaneous. In order to intelligently understand text and extract a wide range of information, it is useful to more prec...