We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.
We introduce a unified probabilistic approach for deep continual learning based on variational Bayesian inference with open set recognition.
We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training.
The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles.
Notwithstanding, almost all existing classifiers to date were mostly developed for the closed-set scenario, i. e., the classification setup in which it is assumed that all test samples belong to one of the classes with which the classifier was trained.
Photo-identification (photo-id) of dolphin individuals is a commonly used technique in ecological sciences to monitor state and health of individuals, as well as to study the social structure and distribution of a population.