New SWIFT method enhances semi-supervised few-shot learning with VLMs

A new paper proposes SWIFT (Stage-Wise Finetuning with Temperatures), a method to improve semi-supervised few-shot learning (SSFSL) by leveraging open-source vision-language models (VLMs) and publicly available data. Existing SSFSL methods have underperformed compared to few-shot learning baselines due to VLMs producing flat probability distributions. SWIFT addresses this by using temperature scaling to sharpen softmax outputs, enhancing pseudo-label confidence and supervision signals. The method also incorporates a stage-wise training strategy to manage imbalances and domain gaps in retrieved open-world data. AI

IMPACT Enhances few-shot learning capabilities by leveraging existing VLMs and open data, potentially improving auto-annotation tasks.

RANK_REASON The cluster contains a research paper detailing a new method for semi-supervised few-shot learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Few-shot learning
• Hugging Face
• self-supervised learning
• Stage-Wise Finetuning with Temperatures
• SWIFT
• Tian Liu
• vision-language model

• paper
• model release

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