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 …

Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

Logistic regression is by far the most widely used classifier in real-world applications. In this
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …

Active Learning has emerged as a viable solution for addressing the challenge of labeling
extensive amounts of data in data-intensive applications such as computer vision and neural …

Point-based object localization (POL), which pursues high-performance object sensing
under low-cost data annotation, has attracted increased attention. However, the point …

Multiple-instance active learning (MIAL) is a paradigm to collect sufficient training bags for a
multiple-instance learning (MIL) problem, by selecting and querying the most valuable …

Active learning aims to reduce the labeling effort that is required to train algorithms by
learning an acquisition function selecting the most relevant data for which a label should be …

Autonomous driving systems require huge amounts of data to train. Manual annotation of
this data is time-consuming and prohibitively expensive since it involves human resources …

The major advantage of multiple-instance learning (MIL) applied to a computer-aided
detection (CAD) system is that it allows optimizing the latter with case-level labels instead of …

Active learning deals with develo** methods that select examples that may express data
characteristics in a compact way. For remote sensing image segmentation, the selected …