The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.
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Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.
Ranked #1 on Semi-Supervised Video Object Segmentation on YouTube
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.
Ranked #3 on Visual Object Tracking on YouTube-VOS
This paper tackles the task of semi-supervised video object segmentation, i. e., the separation of an object from the background in a video, given the mask of the first frame.
Ranked #1 on Visual Object Tracking on YouTube-VOS
In our framework, the past frames with object masks form an external memory, and the current frame as the query is segmented using the mask information in the memory.
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation.
Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixel-level similarity and segmentation in an end-to-end manner.
Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence.
Ranked #1 on Youtube-VOS on YouTube-VOS