Bi-CoG: Bi-Consistency-Guided Self-Training for Vision-Language Models

• URL: http://arxiv.org/abs/2510.20477v1
• Date: Thu, 23 Oct 2025 12:16:41 GMT
• Title: Bi-CoG: Bi-Consistency-Guided Self-Training for Vision-Language Models
• Authors: Rui Zhu, Song-Lin Lv, Zi-Kang Wang, Lan-Zhe Guo,
• Abstract summary: We propose a plug-and-play methodology named $underlinetextbfBi-Co$nsistency-$underlinetextbfG$uided Self-Training.<n>Bi-CoG assigns high-quality and low-bias pseudo-labels, by simultaneously exploiting inter-model and intra-model consistency, along with an error-aware dynamic pseudo-label assignment strategy.
• Score: 16.116493934368012
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 these limitations, we propose a simple yet effective plug-and-play methodology named $\underline{\textbf{Bi-Co}}$nsistency-$\underline{\textbf{G}}$uided Self-Training (Bi-CoG), which assigns high-quality and low-bias pseudo-labels, by simultaneously exploiting inter-model and intra-model consistency, along with an error-aware dynamic pseudo-label assignment strategy. Both theoretical analysis and extensive experiments over 14 datasets demonstrate the effectiveness of Bi-CoG, which consistently and significantly improves the performance of existing methods.

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