Video object segmentation is a binary labeling problem aiming to separate foreground object(s) from the background region of a video.
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With these challenges in mind, we propose a novel training method and evaluation metrics for the seed rejection problem.
Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy.
As the volume of our training sets grows, more and more objects are seen moving, thus turning our method into unsupervised (or time-supervised) training to segment primary objects.
Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural networks.
In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task.
Ranked #1 on Video Object Segmentation on FBMS
Self-supervised learning for visual object tracking possesses valuable advantages compared to supervised learning, such as the non-necessity of laborious human annotations and online training.
Unlike in previous works, we use the Hide-and-Seek strategy in pre-training to obtain the best possible results in handling occlusions and segment boundary extraction.
Significant progress has been made in Video Object Segmentation (VOS), the video object tracking task in its finest level.
Although our baseline system is a straightforward combination of standard methods, we obtain state-of-the-art results.