The prevailing paradigm for producing semantic segmentation training data relies on
densely labelling each pixel of each image in the training set, akin to colouring-in books …

Crowdsourcing has been widely established as a means to enable human computation at
large-scale, in particular for tasks that require manual labelling of large sets of data items …

Annotating rich audio data is an essential aspect of training and evaluating machine
listening systems. We approach this task in the context of temporally-complex urban …

Data is the engine of modern computer vision, which necessitates collecting large-scale
datasets. This is expensive, and guaranteeing the quality of the labels is a major challenge …

Computer vision is one of the most fundamental areas of artificial intelligence research. It
has contributed to the tremendous progress in the recent deep learning revolution in AI. In …

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

The crowd-powered systems have been shown to be highly successful in the current decade
to manage collective contribution of online workers for solving different complex tasks. It can …

We introduce a method to greatly reduce the amount of redundant annotations required
when crowdsourcing annotations such as bounding boxes, parts, and class labels. For …

Audio annotation is key to developing machine-listening systems; yet, effective ways to
accurately and rapidly obtain crowdsourced audio annotations is understudied. In this work …

As classifications datasets progressively get larger in terms of label space and number of
examples, annotating them with all labels becomes non-trivial and expensive task. For …