Abstract
In scene text detection, Transformer-based methods have addressed the global feature extraction limitations inherent in traditional convolution neural network-based methods. However, most directly rely on native Transformer attention layers as encoders without evaluating their cross-domain limitations and inherent shortcomings: forgetting important information or focusing on irrelevant representations when modeling long-range dependencies for text detection. The recently proposed state space model Mamba has demonstrated better long-range dependencies modeling through a linear complexity selection mechanism. Therefore, we propose a novel scene text detector based on Mamba that integrates the selection mechanism with attention layers, enhancing the encoder's ability to extract relevant information from long sequences. We adopt the Top\_k algorithm to explicitly select key information and reduce the interference of irrelevant information in Mamba modeling. Additionally, we design a dual-scale feed-forward network and an embedding pyramid enhancement module to facilitate high-dimensional hidden state interactions and multi-scale feature fusion. Our method achieves state-of-the-art or competitive performance on various benchmarks, with F-measures of 89.7\%, 89.2\%, and 78.5\% on CTW1500, TotalText, and ICDAR19ArT, respectively. Codes will be available.
Abstract (translated)
在场景文本检测中，基于Transformer的方法解决了传统卷积神经网络方法固有的全局特征提取局限性。然而，大多数此类方法直接依赖于原生的Transformer注意力层作为编码器，并未对其跨域限制和内在缺点进行评估：即在建模长距离依赖时可能会忽略重要信息或过度关注不相关的表示。最近提出的状态空间模型Mamba通过线性复杂度选择机制展示了更好的长距离依赖建模能力。因此，我们提出了一种基于Mamba的新型场景文本检测器，该检测器将选择机制与注意力层相结合，增强了编码器从长序列中提取相关信息的能力。我们在Mamba建模过程中采用Top_k算法来显式地选择关键信息并减少不相关干扰信息的影响。此外，我们设计了一个双尺度前向网络和一个嵌入金字塔增强模块，以促进高维隐藏状态之间的交互及多尺度特征融合。我们的方法在各种基准测试中均取得了最先进的或竞争性的性能，具体而言，在CTW1500、TotalText和ICDAR19ArT数据集上的F-measure值分别为89.7%、89.2%和78.5%。代码将公开提供。
URL
https://arxiv.org/abs/2512.06657
