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This research introduces an in-context learning framework using Perceiver-style architectures to tackle Multiple Instance Learning (MIL) challenges, particularly in low-label regimes common in computational pathology and satellite imagery. By pretraining on synthetic data generators that capture complementary inductive biases, the model achieves superior performance across twelve benchmarks without requiring task-specific gradient updates at inference time. The approach effectively bridges the gap between flexible models prone to overfitting and rigid ones lacking adaptability, offering a robust solution for bag-structured data tasks.
In-Context Multiple Instance Learning
arXiv:2606.06458v2 Announce Type: replace 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 from a handful of labeled bags. At inference time, classification happens in a single forward pass and requires no gradient updates. We propose and investigate different synthetic data generators for bag-structured data and find that they capture complementary inductive biases. A model pretrained on a mixture of these generators inherits their per-task strengths and achieves the best average performance across twelve MIL benchmarks, outperforming supervised baselines that require task-specific training.
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 from a handful of labeled bags. At inference time, classification happens in a single forward pass and requires no gradient updates. We propose and investigate different synthetic data generators for bag-structured data and find that they capture complementary inductive biases. A model pretrained on a mixture of these generators inherits their per-task strengths and achieves the best average performance across twelve MIL benchmarks, outperforming supervised baselines that require task-specific training.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2606.06458 [cs.LG]
(or arXiv:2606.06458v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.06458 Focus to learn more arXiv-issued DOI via DataCite
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