-
摘要:
目前，短视频已经成为新媒体时代极具有代表性的产物之一，其天然的具有时短、强编辑等特点，使得传统视频分类模型不再适合于短视频分类任务。针对综合短视频分类问题的特点，提出了一种基于深度多模态特征融合的短视频分类算法。所提算法将视觉模态信息和音频模态信息输入到域分离网络中，将整个特征空间划分为所有模态共享的公有域部分及由音频模态和视觉模态分别独有的私有域部分，借助优化域分离网络，最大程度地保留了不同模态特征间的差异性和相似性。在公开的短视频分类数据集上进行实验，证明了所提算法可以有效减少特征融合时的冗余性，并将分类的平均精度提高到0.813。
Abstract:Nowadays, micro-video has become one of the most representative products in the new media era. It has the characteristics of short time and strong editing, which makes the traditional video classification models no longer suitable for micro-video classification task.Based on the characteristics of the micro-video classification problem, the micro-video classification algorithm based on deep multimodal feature fusion is proposed. The proposed algorithm inputs the visual modal information and acoustic modal information into the domain separation network, and divides the entire feature space into a shared domain part shared by all modalities and the private domain part unique to the acoustic and visual modalities respectively. By optimizing the domain separation network, the differences and similarities among different modal features are preserved to the greatest extent. The experiments on the public micro-video classification dataset prove that the proposed algorithm can effectively reduce the redundancy of feature fusion and improve the average classification accuracy to 0.813.
-
Key words:
- micro-video /
- multimodal learning /
- deep network /
- classification /
- feature space
-
图 1 基于深度多模态特征融合的短视频分类网络
Figure 1. Micro-video classification network based on deep multimodal feature fusion
图 2 参数α、β、γ不同取值对短视频分类任务的影响
Figure 2. Influence of different values of parameters α, β, γ on micro-video classification task
表 1 不同网络短视频分类性能对比
Table 1. Performance comparison of micro-video classification in different networks
分类算法 AR AP Micro-F1 Macro-F1 单模态 C3D 0.555 0.568 0.569 0.560 R2+1D 0.622 0.643 0.651 0.631 ResNet3D 0.669 0.692 0.695 0.678 GoogleNet 0.679 0.709 0.719 0.691 I3D 0.706 0.727 0.733 0.715 S3D-G 0.741 0.750 0.755 0.744 多模态 C3D(multimodal) 0.612 0.623 0.632 0.617 I3D(multimodal) 0.751 0.763 0.772 0.755 TSN 0.763 0.772 0.781 0.770 CTSN 0.771 0.780 0.785 0.773 SlowFast Network 0.780 0.782 0.794 0.781 本文算法 0.782 0.795 0.813 0.789 
下载: 导出CSV
表 2 不同网络短视频特征分类性能对比
Table 2. Performance comparison of micro-video feature classification in different networks
方法 AR AP Micro-F1 Macro-F1 I3D(视觉模态) 0.720 0.718 0.733 0.720 I3D(音频模态) 0.328 0.319 0.341 0.328 前期融合 0.751 0.763 0.772 0.755 公有域网络 0.771 0.777 0.781 0.774 私有域网络 0.751 0.744 0.763 0.748 本文算法 0.782 0.795 0.813 0.789
下载: 导出CSV
-
[1] ZHANG J, NIE L, WANG X, et al. Shorter-is-better: Venue category estimation from micro-video[C]//Proceedings of ACM International Conference on Multimedia. New Nork: ACM Press, 2016: 1415-1424. [2] WEI Y, WANG X, GUAN W, et al. Neural multimodal cooperative learning toward micro-video understanding[J]. IEEE Transactions on Image Processing, 2019, 29: 1-14. http://ieeexplore.ieee.org/document/8752281/ [3] NIE L, WANG X, ZHANG J, et al. Enhancing micro-video understanding by harnessing external sounds[C]//Proceedings of ACM International Conference on Multimedia. New Nork: ACM Press, 2017: 1192-1200. [4] JING P, SU Y, NIE L, et al. Low-rank multi-view embedding learning for micro-video popularity prediction[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 30(8): 1519-1532. http://ieeexplore.ieee.org/document/8233154/ [5] LIU S, CHEN Z, LIU H, et al. User-video co-attention network for personalized micro-video recommendation[C]//Proceedings of World Wide Web Conference, 2019: 3020-3026. [6] SHANG S, SHI M, SHANG W, et al. A micro-video recommendation system based on big data[C]//Proceedings of International Conference on Computer and Information Science. Piscataway: IEEE Press, 2016: 1-5. [7] LONG X, GAN C, MELO G D, et al. Attention clusters: Purely attention based local feature integration for video classification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 7834-7843. [8] MA C Y, CHEN M H, KIRA Z, et al. TS-LSTM and temporal-inception: Exploiting spatiotemporal dynamics for activity recognition[J]. Signal Processing: Image Communication, 2019, 71: 76-87. doi: 10.1016/j.image.2018.09.003 [9] TRAN D, BOURDEY L, FERGUS R, et al. Learning spatiotemporal features with 3D convolutional networks[C]//Proceedings of IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 4489-4497. [10] CARREIRA J, ZISSERMAN A, QUO V. Action recognition A new model and the kinetics dataset[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 6299-6308. [11] HARA K, KATAOKA H, SATOH Y. Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and imagenet [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 6546-6555. [12] FEICHTENHOFER C, FAN H, MALIK J, et al. SlowFast networks for video recognition[C]//Proceedings of IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2019: 6202-6211. [13] QIU Z, YAO T, MEI T. Learning spatio-temporal representation with pseudo-3D residual networks[C]//Proceedings of IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 5533-5541. [14] TRAN D, WANG H, TORRESANI L, et al. A closer look at spatiotemporal convolutions for action recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 6450-6459. [15] XIE S, SUN C, HUANG J, et al. Rethinking spatiotemporal feature learning for video understanding[EB/OL]. (2017-12-13)[2020-08-01]. https://arxiv.org/abs/1712.04851. [16] D'MELLO S K, KORY J. A review and meta-analysis of multimodal affect detection systems[J]. ACM Computing Surveys, 2015, 47(3): 193-228. doi: 10.1145/2682899 [17] ZHAI D, CHANG H, SHAN S, et al. Multiview metric learning with global consistency and local smoothness[J]. ACM Transactions on Intelligent Systems and Technology, 2012, 3(3): 1-22. [18] FEICHTENHOFER C, PINZ A, ZISSERMAN A. Convolutional two-stream network fusion for video action recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 1933-1941. [19] FRANKLIN J. The elements of statistical learning: Data mining, inference and prediction[J]. The Mathematical Intelligencer, 2005, 27(2): 83-85. doi: 10.1111/j.1467-985X.2010.00646_6.x/full [20] WANG L, XIONG Y, WANG Z, et al. Temporal segment networks: Towards good practices for deep action recognition[C]//Proceedings of European Conference on Computer Vision. Berlin: Springer, 2016: 20-36. [21] CHOPRA S, HASSELL R, LECUN Y. Learning a similarity metric discriminatively, with application to face verification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2005: 539-546. [22] ZAGORUYKO S, KOMODAKIS N. Learning to compare image patches via convolutional neural networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2015: 4353-4361. [23] BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional Siamese networks for object tracking[C]//Proceedings of European Conference on Computer Vision. Berlin: Springer, 2016: 850-865. [24] VALMADRE J, BERTINETTO L, HENRIQUES J, et al. End-to-end representation learning for correlation filter based tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 2805-2813. [25] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2015: 1-9. -
下载:
点击查看大图
计量
- 文章访问数: 1807
- HTML全文浏览量: 257
- PDF下载量: 300
- 被引次数: 0
