The rapid development of deep learning has made a great progress in image segmentation,
one of the fundamental tasks of computer vision. However, the current segmentation …

We present a new method for open-vocabulary universal image segmentation, which is
capable of performing instance, semantic, and panoptic segmentation under a unified …

Open-vocabulary instance segmentation aims at segmenting novel classes without mask
annotations. It is an important step toward reducing laborious human supervision. Most …

Partially-supervised instance segmentation is a task which requests segmenting objects
from novel categories via learning on limited base categories with annotated masks thus …

Instance segmentation requires labor-intensive manual labeling of the contours of complex
objects in images for training. The labels can also be provided incrementally in practice to …

Existing instance segmentation models learn task-specific information using manual mask
annotations from base (training) categories. These mask annotations require tremendous …

Abstract Referring Expression Segmentation (RES) aims to provide a segmentation mask of
the target object in an image referred to by the text (ie, referring expression). Existing …

Abstract Referring Expression Segmentation (RES) can facilitate pixel-level semantic
alignment between vision and language. Most of the existing RES approaches require …

We present a new weakly-supervised few-shot semantic segmentation setting and a meta-
learning method for tackling the new challenge. Different from existing settings, we leverage …

Weakly-supervised semantic segmentation (WSSS) with image-level labels has been widely
studied to relieve the annotation burden of the traditional segmentation task. In this paper …