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基于Transformer和多模态对齐的非自回归手语翻译技术研究

邵舒羽 杜垚 范晓丽

邵舒羽, 杜垚, 范晓丽. 基于Transformer和多模态对齐的非自回归手语翻译技术研究[J]. 电子与信息学报, 2024, 46(7): 2932-2941. doi: 10.11999/JEIT230801
引用本文: 邵舒羽, 杜垚, 范晓丽. 基于Transformer和多模态对齐的非自回归手语翻译技术研究[J]. 电子与信息学报, 2024, 46(7): 2932-2941. doi: 10.11999/JEIT230801
SHAO Shuyu, DU Yao, FAN Xiaoli. Non-Autoregressive Sign Language Translation Technology Based on Transformer and Multimodal Alignment[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2932-2941. doi: 10.11999/JEIT230801
Citation: SHAO Shuyu, DU Yao, FAN Xiaoli. Non-Autoregressive Sign Language Translation Technology Based on Transformer and Multimodal Alignment[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2932-2941. doi: 10.11999/JEIT230801

基于Transformer和多模态对齐的非自回归手语翻译技术研究

doi: 10.11999/JEIT230801
基金项目: 国家自然科学基金(8210072143),北京市教委科技计划青年项目(KM202210037001)
详细信息
    作者简介:

    邵舒羽:男,副教授,研究方向为信号处理、复杂系统可靠性分析

    杜垚:男,博士生,研究方向为模式识别

    范晓丽:女,高级工程师,研究方向为生物医学信号处理、模式识别

    通讯作者:

    邵舒羽 shaoshuyu@bwu.edu.cn

  • 中图分类号: TN108.4; TP391

Non-Autoregressive Sign Language Translation Technology Based on Transformer and Multimodal Alignment

Funds: The National Natural Science Foundation of China (8210072143), R&D Program of Beijing Municipal Education Commission (KM202210037001)
  • 摘要: 为了解决多模态数据的对齐及手语翻译速度较慢的问题,该文提出一个基于自注意力机制模型Transformer的非自回归手语翻译模型(Trans-SLT-NA),同时引入了对比学习损失函数进行多模态数据的对齐,通过学习输入序列(手语视频)和目标序列(文本)的上下文信息和交互信息,实现一次性地将手语翻译为自然语言。该文所提模型在公开数据集PHOENIX-2014T(德语)、CSL(中文)和How2Sign(英文)上进行实验评估,结果表明该文方法相比于自回归模型翻译速度提升11.6~17.6倍,同时在双语评估辅助指标(BLEU-4)、自动摘要评估指标(ROUGE)指标上也接近自回归模型。
  • 图  1  基于Transformer的连续手语识别和翻译框架

    图  2  Trans-SLT-NA模型总体结构图

    图  3  视频编码器的组成结构

    图  4  使用t-SNE对视频表征向量和文本向量的可视化

    表  1  训练模型使用的数据集信息

    数据集语言训练集验证集测试集总数
    PHOENIX-2014T德语7 0965196428 257
    CSL-Daily中文18 4011 0771 17620 654
    How2Sign英文31 1281 7412 32235 191
    下载: 导出CSV

    表  2  模型在PHOENIX-2014T数据集上的结果

    方法 生成方式 验证集 测试集 推理速度
    BLEU-4 ROUGE BLEU-4 ROUGE
    RNN-based[15] AR 9.94 31.8 9.58 31.8 2.3X
    SLTR-T[5] AR 20.69 20.17 1.0X
    Multi-C[26] AR 19.51 44.59 18.51 43.57
    STMC-T[24] AR 24.09 48.24 23.65 46.65
    PiSLTRc[17] AR 21.48 47.89 21.29 48.13 0.92X
    Trans-SLT-NA NAR 18.81 47.32 19.03 48.22 11.6X
    注:AR表示自回归生成方式,NAR表示非自回归生成。
    下载: 导出CSV

    表  3  CSL-Daily数据集上的对比结果

    方法 生成方式 验证集 测试集 推理速度
    BLEU-4 ROUGE BLEU-4 ROUGE
    SLTR-T[5] AR 11.88 37.06 11.79 36.74 1X
    Sign Back-Tran[19] AR 20.80 49.49 21.34 49.31 0.89X
    ConSLT[27] AR 14.80 41.46 14.53 40.98
    Trans-SLT-NA NAR 16.22 43.74 16.72 44.67 13.4X
    下载: 导出CSV

    表  4  How2Sign数据集上的对比结果

    方法 生成方式 验证集 测试集 推理速度
    BLEU-4 ROUGE BLEU-4 ROUGE
    Baseline AR 8.89 8.03 1X
    Trans-SLT-NA NAR 8.14 32.84 8.58 33.17 17.6X
    下载: 导出CSV

    表  5  多模态数据对齐的有效性验证

    模型数据集数据对齐验证集测试集
    BLEU-4ROUGEBLEU-4ROUGE
    Trans-SLT-NAPHOENIX-2014Tw18.8147.3219.0348.22
    w/o16.0243.2115.9742.85
    CSL-Dailyw16.2243.7416.7244.67
    w/o14.4342.2715.2142.84
    How2Signw8.1432.848.5833.17
    w/o7.8130.168.2330.59
    注:w表示使用数据对齐,w/o表示不使用数据对齐。
    下载: 导出CSV

    表  6  空间Embedding对于模型性能的影响结果

    空间Embedding 预训练 验证集 测试集
    BLEU-4 ROUGE BLEU-4 ROUGE
    VGG-19 w/o 14.42 38.76 14.36 39.17
    ResNet-50 15.57 40.26 15.33 41.17
    EfficientNet-B0 16.32 40.11 16.04 41.27
    VGG-19 w 16.84 43.31 16.17 42.09
    ResNet-50 17.79 45.63 16.93 44.53
    EfficientNet-B0 18.81 47.32 19.03 48.22
    下载: 导出CSV

    表  7  损失函数超参数对于模型性能的结果

    ${\lambda _{\mathrm{p}}}$ ${\lambda _{\mathrm{c}}}$ 验证集 测试集
    BLEU-4 ROUGE BLEU-4 ROUGE
    1 0 16.02 43.21 15.97 42.85
    0.8 0.2 17.37 44.89 16.87 42.46
    0.5 0.5 18.81 47.32 19.03 48.22
    0.2 0.8 18.04 46.17 18.26 47.10
    下载: 导出CSV
  • [1] 闫思伊, 薛万利, 袁甜甜. 手语识别与翻译综述[J]. 计算机科学与探索, 2022, 16(11): 2415–2429. doi: 10.3778/j.issn.1673-9418.2205003.

    YAN Siyi, XUE Wanli, and YUAN Tiantian. Survey of sign language recognition and translation[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(11): 2415–2429. doi: 10.3778/j.issn.1673-9418.2205003.
    [2] 陶唐飞, 刘天宇. 基于手语表达内容与表达特征的手语识别技术综述[J]. 电子与信息学报, 2023, 45(10): 3439–3457. doi: 10.11999/JEIT221051.

    TAO Tangfei and LIU Tianyu. A survey of sign language recognition technology based on sign language expression content and expression characteristics[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3439–3457. doi: 10.11999/JEIT221051.
    [3] DUARTE A, PALASKAR S, VENTURA L, et al. How2Sign: A large-scale multimodal dataset for continuous American sign language[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 2734–2743. doi: 10.1109/CVPR46437.2021.00276.
    [4] 周乐员, 张剑华, 袁甜甜, 等. 多层注意力机制融合的序列到序列中国连续手语识别和翻译[J]. 计算机科学, 2022, 49(9): 155–161. doi: 10.11896/jsjkx.210800026.

    ZHOU Leyuan, ZHANG Jianhua, YUAN Tiantian, et al. Sequence-to-sequence Chinese continuous sign language recognition and translation with multilayer attention mechanism fusion[J]. Computer Science, 2022, 49(9): 155–161. doi: 10.11896/jsjkx.210800026.
    [5] CAMGÖZ N C, KOLLER O, HADFIELD S, et al. Sign language transformers: Joint end-to-end sign language recognition and translation[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020: 10020–10030. doi: 10.1109/CVPR42600.2020.01004.
    [6] HUANG Jie, ZHOU Wengang, ZHANG Qilin, et al. Video-based sign language recognition without temporal segmentation[C]. 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 2257–2264. doi: 10.1609/aaai.v32i1.11903.
    [7] ZHOU Hao, ZHOU Wengang, and LI Houqiang. Dynamic pseudo label decoding for continuous sign language recognition[C]. 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China, 2019: 1282–1287. doi: 10.1109/ICME.2019.00223.
    [8] SONG Peipei, GUO Dan, XIN Haoran, et al. Parallel temporal encoder for sign language translation[C]. 2019 IEEE International Conference on Image Processing (ICIP), Taipei, China, 2019: 1915–1919. doi: 10.1109/ICIP.2019.8803123.
    [9] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]. The 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 6000–6010.
    [10] 路飞, 韩祥祖, 程显鹏, 等. 基于轻量3D CNNs和Transformer的手语识别[J]. 华中科技大学学报:自然科学版, 2023, 51(5): 13–18. doi: 10.13245/j.hust.230503.

    LU Fei, HAN Xiangzu, CHENG Xianpeng, et al. Sign language recognition based on lightweight 3D CNNs and transformer[J]. Journal of Huazhong University of Science and Technology:Natural Science Edition, 2023, 51(5): 13–18. doi: 10.13245/j.hust.230503.
    [11] WANG Hongyu, MA Shuming, DONG Li, et al. DeepNet: Scaling transformers to 1, 000 layers[EB/OL]. https://arxiv.org/abs/2203.00555, 2022.
    [12] KISHORE P V V, KUMAR D A, SASTRY A S C S, et al. Motionlets matching with adaptive kernels for 3-D Indian sign language recognition[J]. IEEE Sensors Journal, 2018, 18(8): 3327–3337. doi: 10.1109/JSEN.2018.2810449.
    [13] XIAO Yisheng, WU Lijun, GUO Junliang, et al. A survey on non-autoregressive generation for neural machine translation and beyond[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(10): 11407–11427. doi: 10.1109/TPAMI.2023.3277122.
    [14] LI Feng, CHEN Jingxian, and ZHANG Xuejun. A survey of non-autoregressive neural machine translation[J]. Electronics, 2023, 12(13): 2980. doi: 3390/electronics12132980.
    [15] CAMGOZ N C, HADFIELD S, KOLLER O, et al. Neural sign language translation[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7784–7793. doi: 10.1109/CVPR.2018.00812.
    [16] ARVANITIS N, CONSTANTINOPOULOS C, and KOSMOPOULOS D. Translation of sign language glosses to text using sequence-to-sequence attention models[C]. 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Sorrento, Italy, 2019: 296–302. doi: 10.1109/SITIS.2019.00056.
    [17] XIE Pan, ZHAO Mengyi, and HU Xiaohui. PiSLTRc: Position-informed sign language transformer with content-aware convolution[J]. IEEE Transactions on Multimedia, 2022, 24: 3908–3919. doi: 10.1109/TMM.2021.3109665.
    [18] CHEN Yutong, WEI Fangyun, SUN Xiao, et al. A simple multi-modality transfer learning baseline for sign language translation[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA, 2022: 5110–5120. doi: 10.1109/CVPR52688.2022.00506.
    [19] ZHOU Hao, ZHOU Wengang, QI Weizhen, et al. Improving sign language translation with monolingual data by sign back-translation[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, 2021: 1316–1325. doi: 10.1109/CVPR46437.2021.00137.
    [20] ZHENG Jiangbin, WANG Yile, TAN Cheng, et al. CVT-SLR: Contrastive visual-textual transformation for sign language recognition with variational alignmen[C]. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 23141–23150. doi: 10.1109/CVPR52729.2023.02216.
    [21] GU Jiatao, BRADBURY J, XIONG Caiming, et al. Non-autoregressive neural machine translation[C]. 6th International Conference on Learning Representations, Vancouver, Canada, 2018. doi: 10.48550/arXiv.1711.02281.
    [22] WANG Yiren, TIAN Fei, HE Di, et al. Non-autoregressive machine translation with auxiliary regularization[C]. The 33rd AAAI Conference on Artificial Intelligence, Honolulu, USA, 2019: 5377–5384. doi: 10.1609/aaai.v33i01.33015377.
    [23] XIE Pan, LI Zexian, ZHAO Zheng, et al. MvSR-NAT: Multi-view subset regularization for non-autoregressive machine translation[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022: 1–10. doi: 10.1109/TASLP.2022.3221043.
    [24] ZHOU HAO, ZHOU Wengang, ZHOU Yun, et al. Spatial-temporal multi-cue network for sign language recognition and translation[J]. IEEE Transactions on Multimedia, 2022, 24: 768–779. doi: 10.1109/TMM.2021.3059098.
    [25] TARRÉS L, GÁLLEGO G I, DUARTE A, et al. Sign language translation from instructional videos[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Vancouver, Canada, 2023: 5625–5635. doi: 10.1109/CVPRW59228.2023.00596.
    [26] CAMGOZ N C, KOLLER O, HADFIELD S, et al. Multi-channel transformers for multi-articulatory sign language translation[C]. ECCV 2020 Workshops on Computer Vision, Glasgow, UK, 2020: 301–319. doi: 10.1007/978-3-030-66823-5_18.
    [27] FU Biao, YE Peigen, ZHANG Liang, et al. A token-level contrastive framework for sign language translation[C]. 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023: 1–5. doi: 10.1109/ICASSP49357.2023.10095466.
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出版历程
  • 收稿日期:  2023-08-01
  • 修回日期:  2023-12-27
  • 网络出版日期:  2024-01-08
  • 刊出日期:  2024-07-29

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