论文标题
搜索高性能功能提取器以获取文本识别网络
Searching a High-Performance Feature Extractor for Text Recognition Network
论文作者
论文摘要
功能提取器在文本识别（TR）中起着至关重要的作用，但是由于昂贵的手动调整，自定义其体系结构的探索相对较少。在这项工作中，受神经体系结构搜索（NAS）的成功启发，我们建议搜索合适的功能提取器。我们通过探索具有良好功能提取器的原理来设计特定于域的搜索空间。该空间包括用于空间模型的3D结构空间和顺序模型的基于转换的空间。由于该空间是巨大且结构复杂的，因此无法应用现有的NAS算法。我们提出了一种两阶段算法，以有效地在空间中进行搜索。在第一阶段，我们将空间切成几个街区，并借助辅助头逐步训练每个块。我们将延迟约束引入第二阶段，并通过自然梯度下降从受过训练的超级网络搜索子网络。在实验中，进行了一系列的消融研究，以更好地了解设计的空间，搜索算法和搜索架构。我们还将所提出的方法与手写和场景TR任务上的各种最新方法进行了比较。广泛的结果表明，我们的方法可以以较小的延迟获得更好的识别性能。
Feature extractor plays a critical role in text recognition (TR), but customizing its architecture is relatively less explored due to expensive manual tweaking. In this work, inspired by the success of neural architecture search (NAS), we propose to search for suitable feature extractors. We design a domain-specific search space by exploring principles for having good feature extractors. The space includes a 3D-structured space for the spatial model and a transformed-based space for the sequential model. As the space is huge and complexly structured, no existing NAS algorithms can be applied. We propose a two-stage algorithm to effectively search in the space. In the first stage, we cut the space into several blocks and progressively train each block with the help of an auxiliary head. We introduce the latency constraint into the second stage and search sub-network from the trained supernet via natural gradient descent. In experiments, a series of ablation studies are performed to better understand the designed space, search algorithm, and searched architectures. We also compare the proposed method with various state-of-the-art ones on both hand-written and scene TR tasks. Extensive results show that our approach can achieve better recognition performance with less latency.
