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Augmentation with Reinforcement Transformation for Low-textured Scientific Sketch Image Retrieval

Summary

This paper proposes Augmentation with Reinforcement Transformation (ART), which formulates augmentation selection as a multi-armed bandit problem to adaptively construct view banks for fine-tuning pretrained models on low-textured sketch images. On Florida manatee sketches, ART achieves 71.40% Top-1 accuracy for transformed-scar retrieval and 55.84% for location-aware retrieval, outperforming random augmentation and frozen CLIP.

SourcearXiv Computer VisionAuthor: Zhiqiang Wang, Sheri L. Barton, Jennifer H. Johnson, Yi He, Xingquan Zhu
Augmentation with Reinforcement Transformation for Low-textured Scientific Sketch Image Retrieval
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[Submitted on 21 Oct 2026]

Title:Augmentation with Reinforcement Transformation for Low-textured Scientific Sketch Image Retrieval

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Abstract:Low-textured sketch images are widely used in scientific applications, including wildlife conservation, archaeological illustration, and forensic composites. Learning from and retrieving such images is challenging because they lack detailed surface textures and visual variations commonly exploited by modern deep learning models. This paper proposes Augmentation with Reinforcement Transformation (ART) to fine-tune pretrained models to learn effective representations for accurate sketch image retrieval. Our key observation is that sketches are particularly sensitive to image transformations, such as rotation, flipping, zooming, and stretching. While some transformations preserve their structural integrity, others may introduce distortions that hinder representation learning. Instead of randomly choosing transformations to construct fine-tuning views, ART formulates augmentation selection as a multi-armed bandit (MAB) problem, where transformations are treated as arms and their effectiveness is continuously evaluated through rewards. This enables ART to adaptively construct reward-guided view banks before self-supervised fine-tuning; the subsequent representation loss does not feed back to ART. On Florida manatee sketches provided by Mote Marine Laboratory, ART achieves 71.40% average Top-1 accuracy for transformed-scar retrieval, compared with 68.83% for random augmentation selection, and 55.84% for location-aware sketch retrieval, compared with 18.18% for frozen CLIP.

Comments: IEEE BigData 2026

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2611.05408 [cs.CV]

(or arXiv:2611.05408v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2611.05408

arXiv-issued DOI via DataCite

Submission history

From: Zhiqiang Wang [view email] [v1] Wed, 21 Oct 2026 02:21:27 UTC (3,641 KB)

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Key points

  • ART models transformation selection as a multi-armed bandit, using reward feedback to adaptively build view banks
  • On Mote Marine Laboratory manatee sketches, ART reaches 71.40% Top-1 for scar retrieval and 55.84% for location-aware retrieval
  • ART outperforms random augmentation (68.83%) and frozen CLIP (18.18%) on the two retrieval tasks
  • The method addresses sketches' sensitivity to transformations like rotation, flipping, zooming, and stretching

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