Hierarchies of concepts are useful in many applications from navigation to organization of
objects. Usually, a hierarchy is created in a centralized manner by employing a group of …

Crowdsourcing for Human Intelligence Tasks (HIT) has been widely used to crowdsource
human knowledge, such as image annotation for machine learning. We use a public …

Current deep learning applications in structural health monitoring (SHM) are mostly related
to surface damage such as cracks and rust. Methods using traditional image processing …

Multi-label image classification aims to predict all possible labels in an image. It is usually
formulated as a partial-label learning problem, since it could be expensive in practice to …

Microtask crowdsourcing has emerged as a cost-effective approach for obtaining large-scale
labeled data. Crowdsourcing platforms, such as MTurk, provide an online marketplace …

Visual classifiers are part of many applications including surveillance, autonomous
navigation and scene understanding. The raw data used to train these classifiers is …

Image multi-label classification datasets are often partially labeled (for each sample, only the
labels on some categories are known). One popular solution for training convolutional …

When acquiring labels from crowdsourcing platforms, a task may be designed to include
multiple labels and the values of each label may belong to a set of various distinct options …

The recent development of deep learning has brought breakthroughs in image denoising.
However, the recovery of image detail, especially high-frequency weak information, still …

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed
proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel …