The deficiency of segmentation labels is one of the main obstacles to semantic
segmentation in the wild. To alleviate this issue, we present a novel framework that …

Computer vision builds a connection between image processing and industrials, bringing
modern perception to the automated manufacture of magnetic tiles. In this article, we …

Deep neural network based methods have made a significant breakthrough in salient object
detection. However, they are typically limited to input images with low resolutions (400x400 …

Recent years have witnessed a big leap in automatic visual saliency detection attributed to
advances in deep learning, especially Convolutional Neural Networks (CNNs). However …

Current 3D segmentation methods heavily rely on large-scale point-cloud datasets, which
are notoriously laborious to annotate. Few attempts have been made to circumvent the need …

Tissue/region segmentation of pathology images is essential for quantitative analysis in
digital pathology. Previous studies usually require full supervision (eg, pixel-level …

Weakly supervised semantic segmentation (WSSS) is a challenging yet important research
field in vision community. In WSSS, the key problem is to generate high-quality pseudo …

Training a fully supervised semantic segmentation network requires a large amount of
expensive pixel-level annotations in manual labor. In this work, we focus on studying the …

Weakly-supervised image segmentation is an important task in computer vision. A key
problem is how to obtain high-quality objects location from an image-level category …

Separating a singing voice from its music accompaniment remains an important challenge in
the field of music information retrieval. We present a unique neural network approach …