Decoupling Tracking and Segmentation: Introducing VOST for Efficient Video Object Segmentation
Bibliographic record
Abstract
We propose VOST, a novel segmentation-bytracking framework for semi-supervised Video Object Segmentation (VOS) that decouples the tracking and segmentation tasks to improve both accuracy and robustness. Our approach employs a state-of-the-art zero-shot tracker (SAMURAI) to generate bounding boxes of the target object in each frame. These cropped regions are then fed into a Vision Transformer (ViT)-based segmentation model trained to segment the object within the bounding box. By isolating the object from distracting background content and similar instances, our model eliminates ambiguity, simplifies the segmentation task, and requires no temporal memory to maintain object consistency. VOST is evaluated using three benchmark datasets, DAVIS16, DAVIS17, and SegTrackV2, achieving state-of-the-art performance. Specifically, VOST reaches an $\mathcal{M}$ score of 92.6 on DAVIS16, 88.9 on DAVIS17, and an $\mathcal{F}$-score of 0.929 on SegTrackV2, outperforming all previous methods. Additionally, VOST achieves a real-time inference speed of 20.6 FPS, offering an efficient and scalable solution for practical VOS applications. These results demonstrate the effectiveness of the segmentation-by-tracking paradigm and its potential as a competitive alternative to memory-based approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".