Learn By An Example Transformer For Domain Generalization In Video Object Segmentation
Bibliographic record
Abstract
Video object segmentation is a challenging task in computer vision. In this task, a learning model should be able to segment and track a specific set of objects through a frame sequence. This set of objects is given from the ground truth of the first frame in a sequence. To achieve domain generalization in this task, the learning model must be trained using a massive, labeled dataset that has almost all kinds of objects that can be seen in any frame sequence. However, such a dataset does not exist because the labeling process for this task is so expensive, as it requires per-pixel labeling for each frame in a given frame sequence. In this paper, we propose a novel learning technique and transformer architecture. This novel learning technique allows the model to learn effectively from a small, labeled dataset. Additionally, the novel architecture allows the model to produce segmentation output as a function of an input example, instead of relying on memorizing the representation of all objects to be segmented. The experiments show the superiority of the proposed model in comparison with state-of-the-art models by $10.6 \%$ when evaluated using out-of-domain frame sequences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".