Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive
objects exhibiting camouflage. The current boom in terms of techniques and applications …

Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …

We present Segment Anything Model 2 (SAM 2), a foundation model towards solving
promptable visual segmentation in images and videos. We build a data engine, which …

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 …

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 …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

We present Cutie a video object segmentation (VOS) network with object-level memory
reading which puts the object representation from memory back into the video object …

Video object segmentation (VOS) aims at segmenting a particular object throughout the
entire video clip sequence. The state-of-the-art VOS methods have achieved excellent …

This paper strives for motion expressions guided video segmentation, which focuses on
segmenting objects in video content based on a sentence describing the motion of the …

We present the content deformation field (CoDeF) as a new type of video representation
which consists of a canonical content field aggregating the static contents in the entire video …