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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Existing template-based trackers usually localize the target in each frame with bounding box, thereby being limited in learning pixel-wise representation and handling complex and non-rigid transformation of the target.
Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided.
This paper explores a new propagation method, uses short-term matching modules to extract the information of the previous frame and apply it in propagation, and proposes the network of Long-Short-Term similarity matching for video object segmentation (LSMOVS) Method: By conducting pixel-level matching and correlation between long-term matching module and short-term matching module with the first frame and previous frame, global similarity map and local similarity map are obtained, as well as feature pattern of current frame and masking of previous frame.
On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance.
Ranked #1 on Unsupervised Video Object Segmentation on DAVIS-2016 (using extra training data)
To further demonstrate the generalization ability of our spatiotemporal learning framework, we extend MATNet to another relevant task: dynamic visual attention prediction (DVAP).
The global transfer module conveys the segmentation information in an annotated frame to a target frame, while the local transfer module propagates the segmentation information in a temporally adjacent frame to the target frame.
Ranked #1 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)
How to make a segmentation model efficiently adapt to a specific video and to online target appearance variations are fundamentally crucial issues in the field of video object segmentation.
Video object segmentation, i. e., the separation of a target object from background in video, has made significant progress on real and challenging videos in recent years.
We treat this as a grouping problem by exploiting object proposals and making a joint inference about grouping over both space and time.
Ranked #1 on Unsupervised Video Object Segmentation on FBMS
We describe our development and demonstrate the use of our solver in a video object segmentation task and meta-learning for few-shot learning.