SI-Net: Multi-Scale Context-Aware Convolutional Block for Speaker Verification

Li, Zhuo; Fang, Ce; Xiao, Runqiu; Wang, Qianqian; Yan, Yonghong

doi:10.1109/asru51503.2021.9688119

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Yu Rong [35] proposed a new context-enhanced and self-attention capsule feature pyramid network, which integrates context enhancement and self-attention modules, uses multi-scale context attributes and channel information feature enhancement, and enhances the robustness of feature representation. However, existing methods often fall short in adequately fusing multi-scale features [36] and in contextual modeling across encoding and decoding phases [37], thereby limiting their learning capability and attention optimization for road extraction tasks. The development of more effective algorithms for remote sensing image processing remains an area of active research.…”

Section: Introduction 1related Workmentioning

confidence: 99%

PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module

Xue,

Ren,

Yin

et al. 2024

Applied Sciences

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In the domain of remote sensing research, the extraction of roads from high-resolution imagery remains a formidable challenge. In this paper, we introduce an advanced architecture called PCCAU-Net, which integrates Pyramid Pathway Input, CoordConv convolution, and Dual-Inut Cross Attention (DCA) modules for optimized performance. Initially, the Pyramid Pathway Input equips the model to identify features at multiple scales, markedly enhancing its ability to discriminate between roads and other background elements. Secondly, by adopting CoordConv convolutional layers, the model achieves heightened accuracy in road recognition and extraction against complex backdrops. Moreover, the DCA module serves dual purposes: it is employed at the encoder stage to efficiently consolidate feature maps across scales, thereby fortifying the model’s road detection capabilities while mitigating false positives. In the skip connection stages, the DCA module further refines the continuity and accuracy of the features. Extensive empirical evaluation substantiates that PCCAU-Net significantly outperforms existing state-of-the-art techniques on multiple benchmarks, including precision, recall, and Intersection-over-Union(IoU). Consequently, PCCAU-Net not only represents a considerable advancement in road extraction research, but also demonstrates vast potential for broader applications, such as urban planning and traffic analytics.

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Section: Introduction 1related Workmentioning

confidence: 99%

PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module

Xue,

Ren,

Yin

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

DS-TDNN: Dual-Stream Time-Delay Neural Network With Global-Aware Filter for Speaker Verification

Li,

Gan,

Lin

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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Explore Long-Range Context Features for Speaker Verification

Zhao

Wang

et al. 2023

Applied Sciences

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Multi-scale context information, especially long-range dependency, has shown to be beneficial for speaker verification (SV) tasks. In this paper, we propose three methods to systematically explore long-range context SV feature extraction based on ResNet and analyze their complementarity. Firstly, the Hierarchical-split block (HS-block) is introduced to enlarge the receptive fields (RFs) and extract long-range context information over the feature maps of a single layer, where the multi-channel feature maps are split into multiple groups and then stacked together. Then, by analyzing the contribution of each location of the convolution kernel to SV, we find the traditional convolution with a square kernel is not effective for long-range feature extraction. Therefore, we propose cross convolution kernel (cross-conv), which replaces the original 3 × 3 convolution kernel with a 1 × 5 and 5 × 1 convolution kernel. Cross-conv further enlarges the RFs with the same FLOPs and parameters. Finally, the Depthwise Separable Self-Attention (DSSA) module uses an explicit sparse attention strategy to capture effective long-range dependencies globally in each channel. Experiments are conducted on the VoxCeleb and CnCeleb to verify the effectiveness and robustness of the proposed system. Experimental results show that the combination of HS-block, cross-conv, and DSSA module achieves better performance than any single method, which demonstrates the complementarity of these three methods.

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SI-Net: Multi-Scale Context-Aware Convolutional Block for Speaker Verification

Cited by 3 publications

References 22 publications

PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module

PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module

DS-TDNN: Dual-Stream Time-Delay Neural Network With Global-Aware Filter for Speaker Verification

Explore Long-Range Context Features for Speaker Verification

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