arXiv:2510.20477v3 Announce Type: replace Abstract: Exploiting unlabeled data through semi-supervised learning (SSL) or leveraging pre-trained models via fine-tuning are two prevailing paradigms for addressing label-scarce scenarios. Recently, growing attention has been given to combining fine-tuning of pre-trained vision-language models (VLMs) with SSL, forming the emerging paradigm of semi-supervised fine-tuning. However, existing methods often suffer from model bias and hyperparameter sensitivity, due to reliance on prediction consistency or pre-defined confidence thresholds. To address the
This paper addresses current limitations in semi-supervised learning for vision-language models, an increasingly critical area given the rapid evolution of multimodal AI capabilities.
Improved semi-supervised fine-tuning techniques can significantly reduce reliance on extensive labeled datasets, lowering development costs and accelerating the deployment of specialized AI applications.
The proposed Bi-CoG method offers a more robust approach to leveraging unlabeled data for VLM fine-tuning, potentially leading to more accurate and reliable AI systems with less human annotation effort.
- · AI developers
- · Companies using specialized VLMs
- · Cloud AI service providers
- · Researchers in computer vision and NLP
- · Data labeling services
More efficient and less resource-intensive training of vision-language models becomes possible.
This could accelerate the development and deployment of advanced multimodal AI applications across various industries.
Reduced barriers to entry for AI development might lead to a more diverse ecosystem of AI applications, including those for niche markets.
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Read at arXiv cs.LG