Abstract Recently, the Segment Anything Model (SAM) emerged as a powerful vision
foundation model which is capable to segment anything in 2D images. This paper aims to …

Scene understanding based on image segmentation is a crucial component of autonomous
vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting …

Analyzing scenes thoroughly is crucial for mobile robots acting in different environments.
Semantic segmentation can enhance various subsequent tasks, such as (semantically …

RGB-D semantic segmentation has attracted increasing attention over the past few years.
Existing methods mostly employ homogeneous convolution operators to consume the RGB …

Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as
independent information and design separate networks for feature extraction from each …

We present a novel boundary-aware loss term for semantic segmentation using an inverse-
transformation network, which efficiently learns the degree of parametric transformations …

The representation of the physical world is an issue that concerns the scientific community
studying computer vision, more and more. Recently, research has focused on modern …

We introduce a new image segmentation task, called Entity Segmentation (ES), which aims
to segment all visual entities (objects and stuffs) in an image without predicting their …

We revisit human motion synthesis, a task useful in various real-world applications, in this
paper. Whereas a number of methods have been developed previously for this task, they are …

Towards holistic understanding of 3D scenes a general 3D segmentation method is needed
that can segment diverse objects without restrictions on object quantity or categories while …