PIMNet：一个并行、迭代和模仿的场景文本识别网络
PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition
摘要：
目前，场景文本识别由于其广泛的应用而受到越来越多的关注。大多数最先进的方法采用具有注意力机制的编码器-解码器框架，其从左到右自回归地生成文本。尽管令人信服的性能，速度是有限的，因为一个接一个的解码策略。与自回归模型相反，非自回归模型预测结果的推理时间要短得多，但准确性大大落后于自回归模型。在本文中，我们提出了一个并行，迭代和模仿网络（PIMNet），以平衡精度和效率。具体而言，PIMNet采用并行注意力机制来更快地预测文本，并采用迭代生成机制来使预测更准确。在每一次迭代中，上下文信息都得到了充分的探索。为了提高隐藏层的学习，我们利用模仿学习在训练阶段，其中一个额外的自回归解码器被采用和并行解码器模仿自回归解码器与隐藏层的拟合输出。通过两个解码器之间的共享骨干，所提出的PIMNet可以在没有预训练的情况下进行端到端的训练。在推理过程中，自回归解码器的分支被移除以获得更快的速度。在公共基准上的大量实验证明了PIMNet的有效性和效率。我们的代码将在https://github.com/Pay20Y/PIMNet上提供。
英文摘要：
Nowadays, scene text recognition has attracted more and more attention due to its various applications. Most state-of-the-art methods adopt an encoder-decoder framework with attention mechanism, which generates text autoregressively from left to right. Despite the convincing performance, the speed is limited because of the one-by-one decoding strategy. As opposed to autoregressive models, non-autoregressive models predict the results in parallel with a much shorter inference time, but the accuracy falls behind the autoregressive counterpart considerably. In this paper, we propose a Parallel, Iterative and Mimicking Network (PIMNet) to balance accuracy and efficiency. Specifically, PIMNet adopts a parallel attention mechanism to predict the text faster and an iterative generation mechanism to make the predictions more accurate. In each iteration, the context information is fully explored. To improve learning of the hidden layer, we exploit the mimicking learning in the training phase, where an additional autoregressive decoder is adopted and the parallel decoder mimics the autoregressive decoder with fitting outputs of the hidden layer. With the shared backbone between the two decoders, the proposed PIMNet can be trained end-to-end without pre-training. During inference, the branch of the autoregressive decoder is removed for a faster speed. Extensive experiments on public benchmarks demonstrate the effectiveness and efficiency of PIMNet. Our code will be available at https://github.com/Pay20Y/PIMNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
