All instance perception tasks aim at finding certain objects specified by some queries such
as category names, language expressions, and target annotations, but this complete field …

Training data for video segmentation are expensive to annotate. This impedes extensions of
end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary …

Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image.
As permutations of instance IDs are also valid solutions, the task requires learning of high …

We introduce a novel method using a new generative model that automatically learns
effective representations of the target and background appearance to detect, segment and …

In recent years, video instance segmentation (VIS) has been largely advanced by offline
models, while online models gradually attracted less attention possibly due to their inferior …

Referring video object segmentation (R-VOS) is an emerging cross-modal task that aims to
segment the target object referred by a language expression in all video frames. In this work …

Video segmentation aims to segment and track every pixel in diverse scenarios accurately.
In this paper, we present Tube-Link, a versatile framework that addresses multiple core tasks …

This paper presents Video K-Net, a simple, strong, and unified framework for fully end-to-
end video panoptic segmentation. The method is built upon K-Net, a method that unifies …

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 …

In this work, we present SeqFormer for video instance segmentation. SeqFormer follows the
principle of vision transformer that models instance relationships among video frames …