用去相关自监督学习提升笔迹识别的特征表达能力
Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification
- 基于去相关机制构建新框架SWIS，增强笔迹特征解耦性
- 在笔迹识别基准上超越现有自监督与部分监督方法
- 首次将自监督学习用于笔迹验证任务，适合手写分析研究者
过去十年间，自监督学习在计算机视觉领域发展迅速。基于去相关的非对比式自监督预训练方法表现出色，性能可媲美有监督及对比式自监督基线。本文探索该范式并应用于笔迹识别中的解耦笔画特征学习。提出一种改进的去相关框架SWIS，通过在现有框架基础上对各维度特征进行标准化，提升特征独立性。实验表明，该框架在笔迹识别基准上优于当前主流自监督学习方法，也超越多个有监督方法。据我们所知，这是首个将自监督学习应用于笔迹验证任务的研究。
原文摘要 · Abstract (English)
Self-supervised learning has developed rapidly over the last decade and has been applied in many areas of computer vision. Decorrelation-based self-supervised pretraining has shown great promise among non-contrastive algorithms, yielding performance at par with supervised and contrastive self-supervised baselines. In this work, we explore the decorrelation-based paradigm of self-supervised learning and apply the same to learning disentangled stroke features for writer identification. Here we propose a modified formulation of the decorrelation-based framework named SWIS which was proposed for signature verification by standardizing the features along each dimension on top of the existing framework. We show that the proposed framework outperforms the contemporary self-supervised learning framework on the writer identification benchmark and also outperforms several supervised methods as well. To the best of our knowledge, this work is the first of its kind to apply self-supervised learning for learning representations for writer verification tasks.
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