The task of semantic segmentation holds a fundamental position in the field of computer
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …

The annotations used during the training process are crucial for the inference results of
remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be …

Video segmentation—partitioning video frames into multiple segments or objects—plays a
critical role in a broad range of practical applications, from enhancing visual effects in movie …

Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …

In recent years, deep neural nets have triumphed over many computer vision problems,
including semantic segmentation, which is a critical task in emerging autonomous driving …

We propose a new method for video object segmentation (VOS) that addresses object
pattern learning from unlabeled videos, unlike most existing methods which rely heavily on …

The recent advancement in Video Instance Segmentation (VIS) has largely been driven by
the use of deeper and increasingly data-hungry transformer-based models. However, video …

Training a deep network to perform semantic segmentation requires large amounts of
labeled data. To alleviate the manual effort of annotating real images, researchers have …

Learning semantic segmentation models requires a huge amount of pixel-wise labeling.
However, labeled data may only be available abundantly in a domain different from the …

State of the art methods for semantic image segmentation are trained in a supervised
fashion using a large corpus of fully labeled training images. However, gathering such a …