arXiv:2606.06458v1 Announce Type: new 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 new tasks f
The proliferation of real-world datasets with limited labeled examples and the concurrent advancements in foundation models and in-context learning capabilities make this research timely.
This development addresses a critical bottleneck in AI deployment by enabling effective learning from scarce labeled data, broadening AI applicability across sensitive sectors like medicine and defence.
AI models can now be effectively trained with significantly fewer labeled examples, reducing the cost and effort of data annotation for many real-world applications.
- · AI researchers
- · Computational pathology
- · Satellite imagery analysis
- · Industries with scarce labeled data
- · Traditional data annotation services
- · AI models reliant on large, perfectly labeled datasets
Reduced data annotation costs and faster AI model deployment for specific applications.
Accelerated development of specialized AI applications in data-poor domains, potentially leading to new product categories.
Democratization of advanced AI capabilities to organizations with limited data resources, fostering broader AI adoption beyond tech giants.
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Read at arXiv cs.LG