Papers › CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Pardis Taghavi, Tian Liu, Renjie Li, Reza Langari, Zhengzhong Tu
Instance segmentation demands costly per-pixel annotations and large models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that compresses pretrained vision foundation models (VFM) into compact experts using limited labeled and abundant unlabeled data. CAST unfolds in three stages: (1) domain adaptation of the VFM teacher(s) via self-training with contrastive pixel calibration, (2) distillation into a compact student via a unified multi-objective loss that couples standard supervision and pseudo-labels with our instance-aware pixel-wise contrastive term, and (3) fine-tuning on labeled data to remove residual pseudo-label bias. Central to CAST is an \emph{instance-aware pixel-wise contrastive loss} that fuses mask and class scores to mine informative negatives and enforce clear inter-instance margins. By maintaining this contrastive signal across both adaptation and distillation, we align teacher and student embeddings and fully leverage unlabeled images. On Cityscapes and ADE20K, our ~11X smaller student surpasses its adapted VFM teacher(s) by +3.4 AP (33.9 vs. 30.5) and +1.5 AP (16.7 vs. 15.2) and outperforms state-of-the-art semi-supervised approaches.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instance Segmentation | Cityscapes | CAST | AP | 33.9 | #1 of 1 | Archive leaderboard | report |
| Knowledge Distillation | Cityscapes | CAST | AP | 33.9 | #1 of 1 | Archive leaderboard | report |
| Semi-Supervised Instance Segmentation | ADE20K | CAST | AP | 16.7 | #1 of 1 | Archive leaderboard | report |
| Semi-Supervised Instance Segmentation | Cityscapes | CAST | AP | 33.9 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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