The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …

Supervised learning techniques construct predictive models by learning from a large
number of training examples, where each training example has a label indicating its ground …

Automated Screening of COVID-19 from chest CT is of emergency and importance during
the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 …

Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …

Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such
as 3D reconstruction, we have not attained the same level of success in high-level scene …

We have witnessed great interest and a wealth of promise in content-based image retrieval
as an emerging technology. While the last decade laid foundation to such promise, it also …

Time series classification is an important task with many challenging applications. A nearest
neighbor (NN) classifier with dynamic time warping (DTW) distance is a strong solution in …

Automatic linguistic indexing of pictures is an important but highly challenging problem for
researchers in computer vision and content-based image retrieval. In this paper, we …

Multiple-instance problems arise from the situations where training class labels are attached
to sets of samples (named bags), instead of individual samples within each bag (called …

Previous studies on multi-instance learning typically treated instances in the bags as
independently and identically distributed. The instances in a bag, however, are rarely …