用于连续手语识别的多尺度情境感知网络

Q1 Computer Science
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引用次数: 0

摘要

手和脸是手语视频中表达手语语素的最重要部分。然而,我们发现现有的连续手语识别(CSLR)方法缺乏对视觉骨干中手和脸部信息的挖掘,或者使用昂贵耗时的外部提取器来挖掘这些信息。此外,手势的长度各不相同,而以往的 CSLR 方法通常使用固定长度的窗口分割视频以捕捉连续特征,然后进行全局时序建模,这干扰了对完整手势的感知。在本研究中,我们提出了一种多尺度上下文感知网络(MSCA-Net)来解决上述问题。我们的 MSCA-Net 包含两个主要模块:(1) 多尺度运动注意(MSMA),它利用帧间的差异来感知多个空间尺度上的手部和面部信息,取代了繁重的特征提取器;(2) 多尺度时间建模(MSTM),它从不同的时间尺度上探索手语视频中关键的时间信息。我们使用三个广泛使用的手语数据集(即 RWTH-PHOENIX-Weather-2014、RWTH-PHOENIX-Weather-2014T 和 CSL-Daily)进行了大量实验。所提出的 MSCA-Net 达到了最先进的性能,证明了我们方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-scale context-aware network for continuous sign language recognition

The hands and face are the most important parts for expressing sign language morphemes in sign language videos. However, we find that existing Continuous Sign Language Recognition (CSLR) methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information. In addition, the signs have different lengths, whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling, which disturbs the perception of complete signs. In this study, we propose a Multi-Scale Context-Aware network (MSCA-Net) to solve the aforementioned problems. Our MSCA-Net contains two main modules: (1) Multi-Scale Motion Attention (MSMA), which uses the differences among frames to perceive information of the hands and face in multiple spatial scales, replacing the heavy feature extractors; and (2) Multi-Scale Temporal Modeling (MSTM), which explores crucial temporal information in the sign language video from different temporal scales. We conduct extensive experiments using three widely used sign language datasets, i.e., RWTH-PHOENIX-Weather-2014, RWTH-PHOENIX-Weather-2014T, and CSL-Daily. The proposed MSCA-Net achieve state-of-the-art performance, demonstrating the effectiveness of our approach.

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来源期刊
Virtual Reality  Intelligent Hardware
Virtual Reality Intelligent Hardware Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.40
自引率
0.00%
发文量
35
审稿时长
12 weeks
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