Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Object segmentation and object tracking are fundamental research areas in the computer
vision community. These two topics are difficult to handle some common challenges, such …

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 …

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 …

In this paper we illustrate how to perform both visual object tracking and semi-supervised
video object segmentation, in real-time, with a single simple approach. Our method, dubbed …

We introduce a novel network, called as CO-attention Siamese Network (COSNet), to
address the unsupervised video object segmentation task from a holistic view. We …

We present a method that decomposes, and" unwraps", an input video into a set of layered
2D atlases, each providing a unified representation of the appearance of an object (or …

In this paper we introduce a Transformer-based approach to video object segmentation
(VOS). To address compounding error and scalability issues of prior work, we propose a …

We present a network architecture for processing point clouds that directly operates on a
collection of points represented as a sparse set of samples in a high-dimensional lattice …

Learning long-term spatial-temporal features are critical for many video analysis tasks.
However, existing video segmentation methods predominantly rely on static image …