Oracle Character Recognition using Unsupervised Discriminative Consistency Network

• URL: http://arxiv.org/abs/2312.06075v1
• Date: Mon, 11 Dec 2023 02:52:27 GMT
• Title: Oracle Character Recognition using Unsupervised Discriminative Consistency Network
• Authors: Mei Wang, Weihong Deng, Sen Su
• Abstract summary: We propose a novel unsupervised domain adaptation method for oracle character recognition (OrCR) We leverage pseudo-labeling to incorporate the semantic information into adaptation and constrain augmentation consistency. Our approach achieves state-of-the-art result on Oracle-241 dataset and substantially outperforms the recently proposed structure-texture separation network by 15.1%.
• Score: 65.64172835624206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ancient history relies on the study of ancient characters. However, real-world scanned oracle characters are difficult to collect and annotate, posing a major obstacle for oracle character recognition (OrCR). Besides, serious abrasion and inter-class similarity also make OrCR more challenging. In this paper, we propose a novel unsupervised domain adaptation method for OrCR, which enables to transfer knowledge from labeled handprinted oracle characters to unlabeled scanned data. We leverage pseudo-labeling to incorporate the semantic information into adaptation and constrain augmentation consistency to make the predictions of scanned samples consistent under different perturbations, leading to the model robustness to abrasion, stain and distortion. Simultaneously, an unsupervised transition loss is proposed to learn more discriminative features on the scanned domain by optimizing both between-class and within-class transition probability. Extensive experiments show that our approach achieves state-of-the-art result on Oracle-241 dataset and substantially outperforms the recently proposed structure-texture separation network by 15.1%.

Related papers

• Unsupervised Attention Regularization Based Domain Adaptation for Oracle Character Recognition [59.05212866862219]
  The study of oracle characters plays an important role in Chinese archaeology and philology. The difficulty of collecting and annotating real-world scanned oracle characters hinders the development of oracle character recognition. We develop a novel unsupervised domain adaptation (UDA) method to transfer recognition knowledge from labeled handprinted oracle characters to unlabeled scanned data.
  arXiv  Detail & Related papers  (2024-09-24T09:07:05Z)
• A Global and Patch-wise Contrastive Loss for Accurate Automated Exudate Detection [12.669734891001667]
  Diabetic retinopathy (DR) is a leading global cause of blindness. Early detection of hard exudates plays a crucial role in identifying DR, which aids in treating diabetes and preventing vision loss. We present a novel supervised contrastive learning framework to optimize hard exudate segmentation.
  arXiv  Detail & Related papers  (2023-02-22T17:39:00Z)
• Unsupervised Structure-Texture Separation Network for Oracle Character Recognition [70.29024469395608]
  Oracle bone script is the earliest-known Chinese writing system of the Shang dynasty and is precious to archeology and philology. We propose a structure-texture separation network (STSN), which is an end-to-end learning framework for joint disentanglement, transformation, adaptation and recognition.
  arXiv  Detail & Related papers  (2022-05-13T10:27:02Z)
• Feature Diversity Learning with Sample Dropout for Unsupervised Domain Adaptive Person Re-identification [0.0]
  This paper proposes a new approach to learn the feature representation with better generalization ability through limiting noisy pseudo labels. We put forward a brand-new method referred as to Feature Diversity Learning (FDL) under the classic mutual-teaching architecture. Experimental results show that our proposed FDL-SD achieves the state-of-the-art performance on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2022-01-25T10:10:48Z)
• Semi-supervised Long-tailed Recognition using Alternate Sampling [95.93760490301395]
  Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. We propose a new recognition setting, namely semi-supervised long-tailed recognition. We demonstrate significant accuracy improvements over other competitive methods on two datasets.
  arXiv  Detail & Related papers  (2021-05-01T00:43:38Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis [76.46004354572956]
  We introduce an unsupervised domain adaptation approach for person re-identification. Experimental results show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance.
  arXiv  Detail & Related papers  (2020-01-14T17:43:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.