论文标题
探索更快的本地化学习以进行场景文本检测
Explore Faster Localization Learning For Scene Text Detection
论文作者
论文摘要
通常，对于基于深网的良好表现探测器获得良好的训练和长期培训计算是必要的。在本文中，我们提出了一个新的场景文本检测网络（称为狂热者），其快速收敛速度和准确的文本本地化。提出的粉丝是基于变压器特征学习和标准化的傅立叶描述符建模的端到端文本检测器，其中傅立叶描述符建议网络和迭代文本解码网络旨在有效，准确地识别文本建议。此外，还提出了一个密集的匹配策略和精心设计的损失函数，以优化网络性能。进行了广泛的实验，以证明所提出的粉丝可以通过更少的训练时期和没有预训练来实现SOTA性能。当我们引入其他数据进行预训练时，提出的粉丝可以在MSRATD500，CTW1500和TotalText上实现SOTA性能。消融实验还验证了我们贡献的有效性。
Generally pre-training and long-time training computation are necessary for obtaining a good-performance text detector based on deep networks. In this paper, we present a new scene text detection network (called FANet) with a Fast convergence speed and Accurate text localization. The proposed FANet is an end-to-end text detector based on transformer feature learning and normalized Fourier descriptor modeling, where the Fourier Descriptor Proposal Network and Iterative Text Decoding Network are designed to efficiently and accurately identify text proposals. Additionally, a Dense Matching Strategy and a well-designed loss function are also proposed for optimizing the network performance. Extensive experiments are carried out to demonstrate that the proposed FANet can achieve the SOTA performance with fewer training epochs and no pre-training. When we introduce additional data for pre-training, the proposed FANet can achieve SOTA performance on MSRATD500, CTW1500 and TotalText. The ablation experiments also verify the effectiveness of our contributions.
