Abstract Deep Supervised Salient Object Detection (SSOD) excessively relies on large-
scale annotated pixel-level labels which consume intensive labour acquiring high quality …

In unsupervised domain adaptation (UDA) of remote sensing images (RSIs), the huge
interdomain discrepancies and intradomain variances lead to complicated class-level …

The tasks of few-shot, one-shot, and zero-shot learning—or collectively “low-shot
learning”(LSL)—at first glance are quite similar to the long-standing task of class imbalanced …

In an era of parallel computing, evolutionary multitasking optimization (EMT) has become a
popular optimization paradigm due to its ability to optimize several tasks simultaneously …

In recent years, the accurate recognition of traffic scenes has played a key role in
autonomous vehicle operations. However, most works in this area do not address the …

In traditional machine learning, there is often a discrepancy in data distribution between the
source and target domains. Domain adaptation (DA) was proposed to learn the robust …

Lung cancer constitutes the most severe cause of cancer-related mortality. Recent evidence
supports that early detection by means of computed tomography (CT) scans significantly …

Recently, nonshared-and-imbalanced unsupervised domain adaption has been proposed to
fix domain shift from Big Data source domain with long-tail distribution to specific small target …

Regardless of how many classes a machine learning model had seen during the training
procedure, it is inevitable that unexpected and unknown objects will appear during the …