Transformers, the dominant architecture for natural language processing, have also recently
attracted much attention from computational visual media researchers due to their capacity …

As one of the fundamental problems in the field of video understanding, video object
segmentation aims at segmenting objects of interest throughout the given video sequence …

We present XMem, a video object segmentation architecture for long videos with unified
feature memory stores inspired by the Atkinson-Shiffrin memory model. Prior work on video …

Transformer trackers have achieved impressive advancements recently, where the attention
mechanism plays an important role. However, the independent correlation computation in …

It is a challenging task to learn discriminative representation from images and videos, due to
large local redundancy and complex global dependency in these visual data. Convolution …

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 …

Modern hierarchical vision transformers have added several vision-specific components in
the pursuit of supervised classification performance. While these components lead to …

In this paper, we study masked autoencoder (MAE) pretraining on videos for matching-
based downstream tasks, including visual object tracking (VOT) and video object …

This paper investigates how to realize better and more efficient embedding learning to tackle
the semi-supervised video object segmentation under challenging multi-object scenarios …

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 …