Pixels to Tokens: Token Space Efficient Active Learning for Low-Budget Semantic Segmentation
While foundation models and vision transformers (ViTs) have redefined visual representations, current active learning (AL) strategies for these backbones largely operate at the patch or region level, where annotation requirements remain high due to extensive pixel labeling. Extending ViT-based AL to the pixel-level, low-budget regime is uniquely challenging: with as little as one pixel query per image, a method must simultaneously identify the most informative locations and train a reliable decoder head from an extremely sparse signal. We introduce TEAL (Token-space Efficient Active Learning), the first pixel-level AL framework built around ViT representations. TEAL uses a frozen ViT back...
| Authors | Utku Cicek, Mahip Singh, Ziyao Shang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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