Image segmentation, which has become a research hotspot in the field of image processing
and computer vision, refers to the process of dividing an image into meaningful and non …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Instance segmentation is a fundamental vision task that aims to recognize and segment
each object in an image. However, it requires costly annotations such as bounding boxes …

Clustering is a class of unsupervised learning methods that has been extensively applied
and studied in computer vision. Little work has been done to adapt it to the end-to-end …

Learning-based perception and prediction modules in modern autonomous driving systems
typically rely on expensive human annotation and are designed to perceive only a handful of …

This paper studies the problem of learning semantic segmentation from image-level
supervision only. Current popular solutions leverage object localization maps from …

The usage of convolutional neural networks (CNNs) for unsupervised image segmentation
was investigated in this study. Similar to supervised image segmentation, the proposed CNN …

Semantic segmentation has a broad range of applications, but its real-world impact has
been significantly limited by the prohibitive annotation costs necessary to enable …

In this article, we model a set of pixelwise object segmentation tasks—automatic video
segmentation (AVS), image co-segmentation (ICS) and few-shot semantic segmentation …