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Found 4 papers, 2 papers with code
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
In this study, we explore a universal model that can flexibly adapt to unseen dense label structures with a few examples, enabling it to serve as a data-efficient vision generalist in diverse real-world scenarios.
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching
We propose Visual Token Matching (VTM), a universal few-shot learner for arbitrary dense prediction tasks.
Multi-Task Neural Processes
To this end, we propose Multi-Task Neural Processes (MTNPs), an extension of NPs designed to jointly infer tasks realized from multiple stochastic processes.
Multi-Task Processes
Neural Processes (NPs) consider a task as a function realized from a stochastic process and flexibly adapt to unseen tasks through inference on functions.
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