突破噪声对应:图像文本匹配的稳健模型

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Haitao Shi, Meng Liu, Xiaoxuan Mu, Xuemeng Song, Yupeng Hu, Liqiang Nie
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引用次数: 0

摘要

在现实世界的应用中,图像-文本匹配的强大功能受到了噪声对应的阻碍。手动整理高质量的数据集既昂贵又耗时,而使用扩散模型生成的数据集也没有充分的匹配。最有前途的方法是从互联网上收集图像-文本对,但这不可避免地会引入噪声对应。为了减少噪声对应带来的负面影响,我们提出了一种新型模型,利用预训练模型的强大功能,首先将噪声对应过滤问题转化为相似性分布建模问题。具体来说,我们使用高斯混合模型将 CLIP 得到的相似性分为干净分布和噪声分布,从而过滤掉数据集中的大部分噪声对应关系。之后,我们使用相对干净的数据对模型进行微调。为了进一步降低未过滤噪声对应的负面影响,即微调过程中两个分布相交的最小部分,我们提出了分布敏感的动态边际排序损失,进一步增加两个分布之间的距离。通过不断迭代,噪声对应关系逐渐减小,模型性能逐渐提高。我们的大量实验证明,即使在高噪声率下,我们的模型也是有效和稳健的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breaking Through the Noisy Correspondence: A Robust Model for Image-Text Matching

Unleashing the power of image-text matching in real-world applications is hampered by noisy correspondence. Manually curating high-quality datasets is expensive and time-consuming, and datasets generated using diffusion models are not adequately well-aligned. The most promising way is to collect image-text pairs from the Internet, but it will inevitably introduce noisy correspondence. To reduce the negative impact of noisy correspondence, we propose a novel model that first transforms the noisy correspondence filtering problem into a similarity distribution modeling problem by exploiting the powerful capabilities of pre-trained models. Specifically, we use the Gaussian Mixture model to model the similarity obtained by CLIP as clean distribution and noisy distribution, to filter out most of the noisy correspondence in the dataset. Afterward, we used relatively clean data to fine-tune the model. To further reduce the negative impact of unfiltered noisy correspondence, i.e., a minimal part where two distributions intersect during the fine-tuning process, we propose a distribution-sensitive dynamic margin ranking loss, further increasing the distance between the two distributions. Through continuous iteration, the noisy correspondence gradually decreases and the model performance gradually improves. Our extensive experiments demonstrate the effectiveness and robustness of our model even under high noise rates.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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