Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Recent successes in learning-based image classification, however, heavily rely on the large
number of annotated training samples, which may require considerable human effort. In this …

As an interesting and emerging topic, co-saliency detection aims at simultaneously
extracting common salient objects from a group of images. On one hand, traditional co …

Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

For time series prediction tasks, the autoregressive integrated moving average (ARIMA)
model is one of the most classical and popular linear models, and extended applications …

Curriculum learning is a technique to improve a model performance and generalization
based on the idea that easy samples should be presented before difficult ones during …

This paper aims to develop a novel cost-effective framework for face identification, which
progressively maintains a batch of classifiers with the increasing face images of different …

Co-training is a well-known semi-supervised learning approach which trains classifiers on
two different views and exchanges labels of unlabeled instances in an iterative way. During …