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1.3.0

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@clemsgrs clemsgrs released this 12 Jun 22:28
1.3.0
c7ddfdd

1.3.0 — Decoder-Free Attention Segmentation

This release adds support for decoder-free semantic segmentation from foundation-model attention maps, (only slightly) adapted from https://arxiv.org/abs/2602.18747

Highlights

  • Decoder-free attention-map pixel-classifier segmentation (#73) — frozen ViT CLS/register self-attention is treated as a dense feature grid and classified per pixel, without training a decoder.
  • Swappable pixel classifiers (#73) — added xgboost, random_forest, logistic, and pointwise mlp classifiers with class-stratified pixel sampling.
  • Multi-encoder dense feature composition (#73) — multiple encoder outputs can be cached separately, aligned, concatenated, and used together for segmentation.
  • Documented attention segmentation workflow (#73) — added notes on native-spacing sliding-window inference and how soma differs from the resize-based paper behavior.

Notes

This extends the segmentation stack introduced in v1.2.0 with a lightweight decoder-free path for attention-based pixel classification.

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