In-Context Multiple Instance Learning
Quick summary
arXiv:2606.06458v2 Announce Type: replace-cross Abstract: Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery. Nevertheless, existing algorithms struggle in the low-label regime that characterizes many real-world applications. Flexible models overfit and rigid ones fail to adapt to the task at hand. We show that pretraining an in-context learner with a Perceiver-style architecture on synthetic data yields a model that can solve n
Key takeaways
- arXiv:2606.06458v2 Announce Type: replace-cross Abstract: Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery.
- Nevertheless, existing algorithms struggle in the low-label regime that characterizes many real-world applications.
- Flexible models overfit and rigid ones fail to adapt to the task at hand.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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