Thin-plate spline (TPS) is a principal warp that allows for representing
elastic, nonlinear transformation with control point motions. With the increase
of control points, the warp becomes increasingly flexible but usually
encounters a bottleneck caused by undesired issues, e.g., content distortion.
In this paper, we explore generic applications of TPS in single-image-based
warping tasks, such as rotation correction, rectangling, and portrait
correction. To break this bottleneck, we propose the coupled thin-plate spline
model (CoupledTPS), which iteratively couples multiple TPS with limited control
points into a more flexible and powerful transformation. Concretely, we first
design an iterative search to predict new control points according to the
current latent condition. Then, we present the warping flow as a bridge for the
coupling of different TPS transformations, effectively eliminating
interpolation errors caused by multiple warps. Besides, in light of the
laborious annotation cost, we develop a semi-supervised learning scheme to
improve warping quality by exploiting unlabeled data. It is formulated through
dual transformation between the searched control points of unlabeled data and
its graphic augmentation, yielding an implicit correction consistency
constraint. Finally, we collect massive unlabeled data to exhibit the benefit
of our semi-supervised scheme in rotation correction. Extensive experiments
demonstrate the superiority and universality of CoupledTPS over the existing
state-of-the-art (SoTA) solutions for rotation correction and beyond. The code
and data will be available at this https URL

该研究探索了薄板样条（TPS）在单图像变形任务中的应用，提出了耦合薄板样条模型（CoupledTPS），通过迭代耦合多个 TPS 变换，并利用半监督学习方案改进变形质量，实现了旋转矫正与更强大变换的优越性和普适性的展示。

半监督耦合薄板样条模型用于旋转校正与更多

Semi-Supervised Coupled Thin-Plate Spline Model for Rotation Correction  and Beyond

Text irregularities pose significant challenges to scene text recognizers.
Thin-Plate Spline (TPS)-based rectification is widely regarded as an effective
means to deal with them. Currently, the calculation of TPS transformation
parameters purely depends on the quality of regressed text borders. It ignores
the text content and often leads to unsatisfactory rectified results for
severely distorted text. In this work, we introduce TPS++, an
attention-enhanced TPS transformation that incorporates the attention mechanism
to text rectification for the first time. TPS++ formulates the parameter
calculation as a joint process of foreground control point regression and
content-based attention score estimation, which is computed by a dedicated
designed gated-attention block. TPS++ builds a more flexible content-aware
rectifier, generating a natural text correction that is easier to read by the
subsequent recognizer. Moreover, TPS++ shares the feature backbone with the
recognizer in part and implements the rectification at feature-level rather
than image-level, incurring only a small overhead in terms of parameters and
inference time. Experiments on public benchmarks show that TPS++ consistently
improves the recognition and achieves state-of-the-art accuracy. Meanwhile, it
generalizes well on different backbones and recognizers. Code is at
this https URL

本文介绍了一种新的 TPS++ 变换，利用注意力机制进行文本纠正，提高了对视觉文本不规则性的处理能力并在公共基准测试中实现了最先进的识别精度。