In this paper,an Enhanced Self-Attention (ESA) mechanism has been put forward
for robust feature extraction.The proposed ESA is integrated with the recursive
gated convolution and self-attention mechanism.In particular, the former is
used to capture multi-order feature interaction and the latter is for global
feature extraction.In addition, the location of interest that is suitable for
inserting the ESA is also worth being explored.In this paper, the ESA is
embedded into the encoder layer of the Transformer network for automatic speech
recognition (ASR) tasks, and this newly proposed model is named GNCformer. The
effectiveness of the GNCformer has been validated using two datasets, that are
Aishell-1 and HKUST.Experimental results show that, compared with the
Transformer network,0.8%CER,and 1.2%CER improvement for these two mentioned
datasets, respectively, can be achieved.It is worth mentioning that only 1.4M
additional parameters have been involved in our proposed GNCformer.

本文提出了增强的自注意机制（ESA）用于鲁棒特征提取，并将其融合到 Transformer 网络的编码层中进行自动语音识别（ASR）任务，这一新模型被命名为 GNCformer。通过 Aishell-1 和 HKUST 数据集的实验证明，GNCformer 相较于 Transformer 网络，能分别提高 0.8% 和 1.2％的字符错误率（CER），并且只增加了 1.4M 的额外参数量。

GNCformer 增强的自注意力机制用于自动语音识别

GNCformer Enhanced Self-attention for Automatic Speech Recognition

Recent progress in vision Transformers exhibits great success in various
tasks driven by the new spatial modeling mechanism based on dot-product
self-attention. In this paper, we show that the key ingredients behind the
vision Transformers, namely input-adaptive, long-range and high-order spatial
interactions, can also be efficiently implemented with a convolution-based
framework. We present the Recursive Gated Convolution
($\textit{g}^\textit{n}$Conv) that performs high-order spatial interactions
with gated convolutions and recursive designs. The new operation is highly
flexible and customizable, which is compatible with various variants of
convolution and extends the two-order interactions in self-attention to
arbitrary orders without introducing significant extra computation.
$\textit{g}^\textit{n}$Conv can serve as a plug-and-play module to improve
various vision Transformers and convolution-based models. Based on the
operation, we construct a new family of generic vision backbones named HorNet.
Extensive experiments on ImageNet classification, COCO object detection and
ADE20K semantic segmentation show HorNet outperform Swin Transformers and
ConvNeXt by a significant margin with similar overall architecture and training
configurations. HorNet also shows favorable scalability to more training data
and larger model sizes. Apart from the effectiveness in visual encoders, we
also show $\textit{g}^\textit{n}$Conv can be applied to task-specific decoders
and consistently improve dense prediction performance with less computation.
Our results demonstrate that $\textit{g}^\textit{n}$Conv can be a new basic
module for visual modeling that effectively combines the merits of both vision
Transformers and CNNs. Code is available at
this https URL

本文提出一种名为 'Recursive Gated Convolution' 的新的可编程高效的卷积操作，它结合了 Vision Transformers 和 CNNs 的优点并能够优化视觉数据的编码和解码，引入这个操作到 HorNet 中能够提高大规模视觉识别、目标检测和图像分割任务的性能。