Object detection typically assumes that training and test data are drawn from an identical
distribution, which, however, does not always hold in practice. Such a distribution mismatch …

Exploiting synthetic data to learn deep models has attracted increasing attention in recent
years. However, the intrinsic domain difference between synthetic and real images usually …

Current semantic segmentation models have achieved great success under the
independent and identically distributed (iid) condition. However, in real-world applications …

Object detection typically assumes that training and test samples are drawn from an identical
distribution, which, however, does not always hold in practice. Such a distribution mismatch …

Morphological reconstruction (MR) is often employed by seeded image segmentation
algorithms such as watershed transform and power watershed, as it is able to filter out seeds …

Rectified linear unit (ReLU) is a widely used activation function for deep convolutional
neural networks. However, because of the zero-hard rectification, ReLU networks lose the …

Universal domain adaptive object detection (UniDAOD) is more challenging than domain
adaptive object detection (DAOD) since the label space of the source domain may not be the …

Remote sensing images can have significant appearance differences due to various factors,
such as atmospheric conditions, sensor types, seasons, and capture times. Therefore, when …

This paper presents SceneCut, a novel approach to jointly discover previously unseen
objects and non-object surfaces using a single RGB-D image. SceneCut's joint reasoning …

Hierarchical image segmentation is a prominent trend in the literature as a way to improve
the segmentation quality. Generally, meaningful objects in an image are described by …