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 …

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 …

Over the years, datasets and benchmarks have proven their fundamental importance in
computer vision research, enabling targeted progress and objective comparisons in many …

We present the 2017 DAVIS Challenge on Video Object Segmentation, a public dataset,
benchmark, and competition specifically designed for the task of video object segmentation …

This paper tackles the task of semi-supervised video object segmentation, ie, the separation
of an object from the background in a video, given the mask of the first frame. We present …

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

We use large amounts of unlabeled video to learn models for visual tracking without manual
human supervision. We leverage the natural temporal coherency of color to create a model …

Inspired by recent advances of deep learning in instance segmentation and object tracking,
we introduce the concept of convnet-based guidance applied to video object segmentation …

This paper proposes an end-to-end trainable network, SegFlow, for simultaneously
predicting pixel-wise object segmentation and optical flow in videos. The proposed SegFlow …