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Found 5 papers, 1 papers with code
AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation
With the proliferation of large language models (LLMs) in the medical domain, there is increasing demand for improved evaluation techniques to assess their capabilities.
Pseudolabel guided pixels contrast for domain adaptive semantic segmentation
Unsupervised domain adaptation (UDA) for semantic segmentation is a technique that uses virtual data with labels to train a model and adapts it to real data without labels.
ACE-$M^3$: Automatic Capability Evaluator for Multimodal Medical Models
As multimodal large language models (MLLMs) gain prominence in the medical field, the need for precise evaluation methods to assess their effectiveness has become critical.
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