用自监督微调提升扫描照片去重效果，显著减少人工标注时间。
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections
- 基于预训练模型在目标数据集上做自监督微调，利用未标注数据增强性能。
- 在UKBench和私有数据集上均优于传统基线方法，准确率明显提升。
- 适合需要处理大规模扫描照片的档案机构或文档管理公司使用。
本文针对某文档管理公司实际需求，开展了一项关于扫描照片中近似重复图像检测的技术对比研究。该场景下部署数据集可预先获取，支持采用归纳式学习（transductive learning）。提出一种结合卷积神经网络（CNN）与视觉变换器（ViTs）的新型方法：先在大规模数据上预训练模型，再在未标注的目标照片集合上通过自监督学习进行微调。实验表明，在UKBench和一个内部私有数据集上，该方法在近似重复图像检测任务中均优于基准方法，有效提升了检测性能。
原文摘要 · Abstract (English)
This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset.
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