State-Aware Tracker is a pipeline for semi-supervised video object segmentation. It takes each target object as a tracklet, which not only makes the pipeline more efficient but also filters distractors to facilitate target modeling. For more stable and robust performance over video sequences, SAT gets awareness for each state and makes self-adaptation via two feedback loops. One loop assists SAT in generating more stable tracklets. The other loop helps to construct a more robust and holistic target representation.
Source: State-Aware Tracker for Real-Time Video Object SegmentationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Semantic Segmentation | 1 | 25.00% |
Semi-Supervised Video Object Segmentation | 1 | 25.00% |
Video Object Segmentation | 1 | 25.00% |
Video Semantic Segmentation | 1 | 25.00% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |