In this work, we propose two ensemble methods leveraging a crowd workforce to improve
video annotation, with a focus on video object segmentation. Their shared principle is that …

Abstract This article presents Epistemic Spatialization as a new framework for investigating
the interconnected patterns of biases when identifying objects with convolutional neural …

Probabilistic topic models are powerful methods to uncover hidden thematic structures in
text by projecting each document into a low dimensional space spanned by a set of topics …

The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels
from a set of concepts. Previous works of MLL mainly focused on the setting where the …

Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early
detection of melanoma using dermoscopy images significantly increases patients' survival …

We propose a new approach to interactive full-image semantic segmentation which enables
quickly collecting training data for new datasets with previously unseen semantic classes (A …

Multi-label classification is a common supervised machine learning problem where each
instance is associated with multiple classes. The key challenge in this problem is learning …

Learning to recognize objects based on names is a crucial capability for personal robots.
Recent recognition methods successfully learn to recognize objects in a train-once-then-test …

Early-stage breast cancer detection remains a critical challenge in healthcare, demanding
innovative approaches that leverage the power of deep learning and transfer learning …

Multi-label classification is a well-known supervised machine learning setting where each
instance is associated with multiple classes. Examples include annotation of images with …