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Found 2 papers, 1 papers with code
Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning
This paper proposes a novel framework for multi-label image recognition without any training data, called data-free framework, which uses knowledge of pre-trained Large Language Model (LLM) to learn prompts to adapt pretrained Vision-Language Model (VLM) like CLIP to multilabel classification.
Bootstrap Generalization Ability from Loss Landscape Perspective
Domain generalization aims to learn a model that can generalize well on the unseen test dataset, i. e., out-of-distribution data, which has different distribution from the training dataset.
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