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 …

Zero-shot remote sensing scene classification aims to solve the scene classification problem
on unseen categories and has attracted numerous research attention in the remote sensing …

The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised
learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a …

Pseudo labeling and consistency regularization approaches with confidence-based
thresholding have made great progress in semi-supervised learning (SSL). In this paper, we …

With the development of the sensor technology, complementary data of different sources can
be easily obtained for various applications. Despite the availability of adequate multisource …

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 …

We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic
curriculum learning, where the Student tries to learn a complex task, and the Teacher …

Human attribute analysis is a challenging task in the field of computer vision. One of the
significant difficulties is brought from largely imbalance-distributed data. Conventional …

Unsupervised domain adaptive object detection aims to adapt detectors from a labelled
source domain to an unlabelled target domain. Most existing works take a two-stage strategy …

With the great success of pre-trained models, the pretrain-then-finetune paradigm has been
widely adopted on downstream tasks for source code understanding. However, compared to …