OSM: An open set matting framework with OOD detection and few-shot learning
Bibliographic record
Abstract
Natural image matting is the task of precisely estimating alpha mattes to separate foreground objects from background images.Existing matting methods only focus on classical closed-set problems where object categories and data distributions are similar between training and test sets.However, in the open world setup, there exists a situation where testing samples are drawn from a different distribution than the training data.To handle this situation, we present the first open set matting (OSM) framework that contains two networks: (1) an out-of-distribution (OOD) detection network to identify OOD to-be-matted objects; and (2) an incremental few-shot learning matting module to enlarge the existing knowledge base of to-be-matted objects.Our OOD detection network leverages metric-based prototype learning to be aware of unseen objects and increase inter-class separability, utilizing intra-batch connections to enhance intra-class compactness.Compared to other OOD detection methods, our network achieves state-of-the-art performance on SIMD dataset.Further, our incremental few-shot learning matting module improves the performance on unseen to-be-matted objects by gradually incorporating novel classes into the existing knowledge base without catastrophic forgetting and overfitting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".