Current referring video object segmentation (R-VOS) techniques extract conditional kernels
from encoded (low-resolution) vision-language features to segment the decoded high …

We present a novel framework, ie, Segment Any Anomaly+(SAA+), for zero-shot anomaly
segmentation with hybrid prompt regularization to improve the adaptability of modern …

Despite the recent advances in unified image segmentation (IS) developing a unified video
segmentation (VS) model remains a challenge. This is mainly because generic category …

Detecting objects based on language information is a popular task that includes Open-
Vocabulary object Detection (OVD) and Referring Expression Comprehension (REC). In this …

The paper introduces PaintSeg, a new unsupervised method for segmenting objects without
any training. We propose an adversarial masked contrastive painting (AMCP) process …

Referring Image Segmentation (RIS) is a fundamental vision-language task that outputs
object masks based on text descriptions. Many works have achieved considerable progress …

Referring video segmentation relies on natural language expressions to identify and
segment objects often emphasizing motion clues. Previous works treat a sentence as a …

Current universal segmentation methods demonstrate strong capabilities in pixel-level
image and video understanding. However, they lack reasoning abilities and cannot be …

Linguistic communication is prevalent in Human-Computer Interaction (HCI). Speech
(spoken language) serves as a convenient yet potentially ambiguous form due to noise and …

Referring video object segmentation (RVOS), as a supervised learning task, relies on
sufficient annotated data for a given scene. However, in more realistic scenarios, only …