Learning From True-False Labels Via Multi-Modal Consistency Collaborative Prompt Tuning

HAN Ridong; XU Jinghao; LI Zhongnian; SUN Tongfeng; XU Xinzheng

doi:10.12263/DZXB.20250727

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Learning From True-False Labels Via Multi-Modal Consistency Collaborative Prompt Tuning

PAPERS | 更新时间：2026-10-16

- Learning From True-False Labels Via Multi-Modal Consistency Collaborative Prompt Tuning
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 2846-2864(2026)
- 作者机构：
  
  1.中国矿业大学计算机科学与技术学院/人工智能学院，江苏徐州 221116
  2.浙江大学计算机辅助设计与图形系统全国重点实验室，浙江杭州 310058
  3.中国矿业大学矿山数字化教育部工程研究中心，江苏徐州 221116
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61976217;62306320);Natural Science Foundation of Jiangsu Province(BK20231063);Open Project Program of the State Key Laboratory of CAD&CG of Zhejiang University(A2424)
- DOI：10.12263/DZXB.20250727
  CLC： TP181;TP391
- Received：20 August 2025，
  
  Accepted：30 April 2026，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
韩日东, 许靖昊, 李仲年, 等. 基于多模态一致性协同提示微调的判断标记学习方法[J]. 电子学报, 2026, 54(06): 2846-2864.

HAN Ridong, XU Jinghao, LI Zhongnian, et al. Learning From True-False Labels Via Multi-Modal Consistency Collaborative Prompt Tuning[J]. Acta Electronica Sinica, 2026, 54(06): 2846-2864.
韩日东, 许靖昊, 李仲年, 等. 基于多模态一致性协同提示微调的判断标记学习方法[J]. 电子学报, 2026, 54(06): 2846-2864. DOI：10.12263/DZXB.20250727

HAN Ridong, XU Jinghao, LI Zhongnian, et al. Learning From True-False Labels Via Multi-Modal Consistency Collaborative Prompt Tuning[J]. Acta Electronica Sinica, 2026, 54(06): 2846-2864. DOI：10.12263/DZXB.20250727

摘要

近期利用大规模未标注数据进行提示学习受到广泛关注。其中，判断标记学习范式通过从候选集中随机采样类别，并借助预训练视觉-语言模型进行二值化（True/False）判断，从而生成高精度的弱监督标记，大幅提升了标注效率，推动了提示学习领域的快速发展。然而，其随机均匀的采样机制不可避免地导致部分类别监督样本缺失，致使下游任务模型难以构建精确的类别语义空间，并且现有方法切断了文本与视觉特征之间的跨模态联合优化路径，破坏了预训练模型原有的跨模态泛化能力。针对上述局限性，本文提出一种基于多模态一致性协同提示微调的判断标记学习方法（true-false labels learning method based on Multi-modal Consistency Collaborative Prompt tuning，MCCP）。该方法在文本和视觉双模态上分别引入一致性约束，并设计了跨模态协同优化机制。具体而言，在文本模态，设计了大语言模型驱动的知识协同机制，利用大语言模型为各个类别生成细粒度的文本描述，通过约束原始文本提示与类别文本描述之间的语义一致性，补齐监督样本缺失类别的语义信息盲区，并且隐式缓解监督信息不足的不利影响；在视觉模态，提出了基于自监督学习的一致性提示学习机制，通过构建多视图，约束原始图像、弱增强视图与强增强视图两两之间的特征分布一致性，迫使视觉提示网络提取对数据增强操作不敏感的深层语义特征，显著提升了视觉语义表征的鲁棒性与泛化能力；在跨模态联合优化方面，引入了基于训练轮次的余弦动态加权策略，以实现预训练先验知识与领域适应性学习表征之间的平滑过渡与渐进式融合，同时采用了分阶段梯度更新策略，分别优化文本、视觉特定模态的一致性损失与跨模态图像分类的经验风险一致性交叉熵损失，有效避免了多模态学习中的梯度冲突问题，促进了跨模态语义空间的深度协同对齐。在CIFAR-100、Tiny-ImageNet、Caltech-101、Food-101、Stanford Cars、DTD（Describable Textures Dataset）、FGVC-Aircraft（Fine-Grained Visual Classification of Aircraft）、ImageNet-A和ImageNet-R九个多类别图像分类数据集上的实验结果表明，该方法在利用对比语言-图像预训练模型（Contrastive Language-Image Pre-training，CLIP）标注和人工标注的两种判断标记数据集上，分别取得了73.58%和74.20%的最优平均分类准确率，同时在跨数据集迁移设置下的绝大多数目标数据集上均展现出最优的迁移泛化性能，验证了该方法的有效性。本文有效解决了判断标记学习范式的监督信号缺失挑战，并为实现高效、鲁棒的多模态跨域知识迁移提供了方法支持。

Abstract

Prompt learning utilizing large-scale unlabeled data has recently received widespread attention. Among these

the true-false label learning paradigm randomly samples a category from the candidate set and leverages pre-trained vision-language models to make binary judgments

i.e.

True or False

thereby generating high-precision weak supervision signals

substantially improving annotation efficiency

and driving the rapid development of the prompt learning field. However

its random uniform sampling mechanism inevitably leads to missing supervision samples for certain categories

making it difficult for downstream task models to construct an accurate category semantic space

and existing methods sever the cross-modal joint optimization path between textual and visual features

destroying the original cross-modal generalization capability of pre-trained models. To address the aforementioned limitations

this paper proposes a true-false labels learning method based on multi-modal consistency collaborative prompt tuning (MCCP). This method introduces consistency constraints in both textual and visual modalities

and designs a cross-modal collaborative optimization strategy. Specifically

in the textual modality

a large language model-driven knowledge collaboration mechanism is designed to generate fine-grained textual descriptions for each category. By constraining the semantic consistency between original textual prompts and category textual descriptions

it fills the semantic information blind spots of categories with missing supervision samples

and implicitly mitigates the effects of insufficient supervision information. In the visual modality

a consistency prompt learning mechanism based on self-supervised learning is proposed. By constructing multiple views

it constrains the pairwise feature distribution consistency among original images

weakly augmented views

and strongly augmented views

thereby forcing the visual prompt network to extract deep semantic features insensitive to data augmentation operations

significantly enhancing the robustness and generalization of visual-semantic representations. In terms of cross-modal joint optimization

an epoch-based cosine dynamic weighting strategy is introduced to achieve a smooth transition and gradual fusion between pre-trained prior knowledge and domain-adaptive feature representations

while a staged gradient update scheme is adopted to separately optimize the consistency losses of text- and vision-specific modalities and the empirical risk-consistency cross-entropy loss of cross-modal image classification

effectively avoiding the gradient conflict problem in multi-modal learning and promoting the deep collaborative alignment of cross-modal semantic spaces. Experimental results on nine multi-class image classification datasets

including CIFAR-100

Tiny-ImageNet

Caltech-101

Food-101

Stanford Cars

describable textures dataset (DTD)

fine-grained visual classification of aircraft (FGVC-Aircraft)

ImageNet-A and ImageNet-R

demonstrate that this method achieves state-of-the-art average classification accuracies of 73.58% and 74.20% on the True-False labels datasets annotated by the contrastive language-image pre-training model (CLIP) and human annotators

respectively

and simultaneously exhibits optimal transfer generalization performance on the vast majority of target datasets under the cross-dataset transfer setting

verifying the effectiveness of MCCP. This paper effectively addresses the missing supervision signal challenge of true-false labels learning paradigm

providing methodological support for achieving efficient and robust multi-modal cross-domain knowledge transfer.

关键词

Keywords

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