Compensating for Scarce Historical Images in Cross-Domain Cultural Heritage Retrieval Using Synthetic Aging

• URL: http://arxiv.org/abs/2609.08766v1
• Date: Tue, 08 Sep 2026 14:01:50 GMT
• Title: Compensating for Scarce Historical Images in Cross-Domain Cultural Heritage Retrieval Using Synthetic Aging
• Abstract summary: This study investigates whether synthetically aged contemporary images can replace or complement missing historical data in bidirectional instance-level retrieval.<n>Mixed real-synthetic training is compared with real-only baselines using proportionally scaled and fixed 300-batch-per-epoch schedules.<n>The results indicate that genuine and synthetic observations are complementary.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cultural heritage collections often contain contemporary and historical visual records of the same physical object. Linking these records is difficult because corresponding images may differ in viewpoint, acquisition conditions, color reproduction, framing, resolution, and degradation, while genuine historical images are frequently scarce. This study investigates whether synthetically aged contemporary images can replace or complement missing historical training data in bidirectional instance-level retrieval. Synthetic old-domain images are generated using degradation-oriented transformations. An EfficientNetV2-M model is evaluated on identity-disjoint training, validation, and test sets across three dataset partitions and three training seeds. Mixed real-synthetic training is compared with real-only baselines using proportionally scaled and fixed 300-batch-per-epoch schedules. Complete replacement of genuine historical images reduced bidirectional mean R@1 from 86.56% to 81.27%, showing that synthetic aging does not reproduce the full genuine old-domain variability. Increasing the number of independently generated synthetic variants provided no consistent improvement. Under controlled scarcity, however, synthetic completion improved mean R@1 by 3.69 percentage points at 25% genuine historical coverage and by 2.92 points at 50%, relative to the proportionally scaled real-only baselines. At 75%, the gain decreased to 2.00 points, while performance remained comparable to the complete-real-data reference. Fixed-schedule real-only controls did not reproduce these improvements. The results indicate that genuine and synthetic observations are complementary. Synthetic completion primarily benefits retrieval by extending cross-domain identity coverage rather than by increasing training exposure, with its contribution gradually decreasing as genuine historical coverage increases.

Related papers

• Combining Synthetic and Real Data for Low-Resource Historical OCR: A Manchu Case Study [7.085654644528252]
  Manchu, now critically endangered, was one of the principal languages of the Qing empire (1636-1912)<n>Previous work showed that vision-language models trained only on synthetic Manchu word images can reach 87.4% word accuracy on real Qing manuscripts and prints.<n>This study examines how synthetic and real historical training data should be combined for low-resource OCR.
  arXiv  Detail & Related papers  (2026-09-10T13:02:11Z)
• Few-Shot Synthetic Data Generation with Diffusion Models for Downstream Vision Tasks [36.94429692322632]
  Class imbalance is a persistent challenge in visual recognition.<n>We propose a lightweight synthetic data augmentation pipeline that fine-tunes a LoRA adapter on as few as 20-50 real images of a rare class.
  arXiv  Detail & Related papers  (2026-05-12T10:11:57Z)
• From Synthetic to Real: Toward Identity-Consistent Makeup Transfer with Synthetic and Real Data [63.40634698361175]
  Makeup transfer aims to apply the makeup style of a reference portrait to a source portrait while preserving identity and background.<n>Recent diffusion- and flow-based approaches instead exploit synthetic data for supervised training, leading to significant improvements.<n>This paper proposes ConsistentBeauty and RealBeauty, a synthetic-to-real post-training framework.
  arXiv  Detail & Related papers  (2026-05-08T15:21:43Z)
• WithAnyone: Towards Controllable and ID Consistent Image Generation [83.55786496542062]
  Identity-consistent generation has become an important focus in text-to-image research.<n>We develop a large-scale paired dataset tailored for multi-person scenarios.<n>We propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity.
  arXiv  Detail & Related papers  (2025-10-16T17:59:54Z)
• Is Synthetic Image Augmentation Useful for Imbalanced Classification Problems? Case-Study on the MIDOG2025 Atypical Cell Detection Competition [2.4582766828986076]
  The MIDOG 2025 challenge extends prior work on mitotic figure detection by introducing a new Track 2 on atypical mitosis classification.<n>This task aims to distinguish normal from atypical figures in histopathology images.<n>We investigated two complementary backbones: (i) ConvNeXt-Small, pretrained on ImageNet, and (ii) a histopathology-specific ViT from Lunit trained via self-supervision.
  arXiv  Detail & Related papers  (2025-08-30T23:37:20Z)
• Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation [0.0]
  StyleGAN was trained on available 3R biopsy patches and subsequently used to generate 10,000 realistic synthetic images.<n>These were combined with real 0R samples, that is samples without rejection, in various configurations to train ResNet-18 classifiers for binary rejection classification.<n>Results demonstrate that synthetic data improves classification performance, particularly when used in combination with real samples.
  arXiv  Detail & Related papers  (2025-05-26T09:26:36Z)
• The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition [10.849598219674132]
  We investigate the impact of demographically balanced authentic and synthetic data, both individually and in combination, on the accuracy and fairness of face recognition models. Our findings emphasize two main points: (i) the increased effectiveness of training data generated by diffusion-based models in enhancing accuracy, whether used alone or combined with subsets of authentic data, and (ii) the minimal impact of incorporating balanced data from pre-trained generative methods on fairness.
  arXiv  Detail & Related papers  (2024-09-04T16:50:48Z)
• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Effective Adapter for Face Recognition in the Wild [72.75516495170199]
  We tackle the challenge of face recognition in the wild, where images often suffer from low quality and real-world distortions. Traditional approaches-either training models directly on degraded images or their enhanced counterparts using face restoration techniques-have proven ineffective. We propose an effective adapter for augmenting existing face recognition models trained on high-quality facial datasets.
  arXiv  Detail & Related papers  (2023-12-04T08:55:46Z)
• TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation [55.94900327396771]
  We introduce neural texture learning for 6D object pose estimation from synthetic data. We learn to predict realistic texture of objects from real image collections. We learn pose estimation from pixel-perfect synthetic data.
  arXiv  Detail & Related papers  (2022-12-25T13:36:32Z)
• Warp Consistency for Unsupervised Learning of Dense Correspondences [116.56251250853488]
  Key challenge in learning dense correspondences is lack of ground-truth matches for real image pairs. We propose Warp Consistency, an unsupervised learning objective for dense correspondence regression. Our approach sets a new state-of-the-art on several challenging benchmarks, including MegaDepth, RobotCar and TSS.
  arXiv  Detail & Related papers  (2021-04-07T17:58:22Z)

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.