Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 1211-1219.doi: 10.13229/j.cnki.jdxbgxb.20210842

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Multi⁃modal self⁃attention network for video memorability prediction

Wei LYU(),Jia-ze HAN,Jing-hui CHU,Pei-guang JING()   

  1. School of Electrical and Information Engineering，Tianjin University，Tianjin 300072，China
  • Received:2021-08-29 Online:2023-04-01 Published:2023-04-20
  • Contact: Pei-guang JING E-mail:luwei@tju.edu.cn;pgjing@tju.edu.cn

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Abstract:

Existing approaches usually encountered with utilizing single-modal features only， insufficient fusion of multi-modal features， or simple semantic representation learning paradigm. To better solve video memorability prediction， an end-to-end deep multi-modal self-attention network is proposed. The proposed network consists of a semantic representation learning sub-network and a semantic modeling sub-network. The semantic representation learning sub-network uses a generative adversarial learning to better explore the associations between modalities and complete the fusion of multi-modal information； the semantic modeling sub-network uses a self-attention mechanism to refine global information while retaining local information to the greatest extent. Experimental results show that the proposed network achieves better prediction performance in comparison with state-of-the-art methods. In addition， the ablation experiments further verify the effectiveness of the two involved sub -networks in video memorability prediction task.

Key words: video memorability, semantic representation, multi-modal fusion, generative adversarial networks, self-attention mechanism

CLC Number: 

  • TP391.4

Fig.1

Architecture diagram of MSPNet"

Fig.2

Semantic representation learning sub-network"

Fig.3

Semantic modeling sub-network"

Table 1

Analysis for single modal experiments"

实验名称Set1Set2Set3Set4平 均
低频模态0.4830.4840.4840.4850.484
中频模态0.4750.4780.4770.4790.477
高频模态0.4680.4660.4660.4670.467

Table 2

Analysis for different modal combination"

实验名称Set1Set2Set3Set4平均
低频+中频0.5090.5100.5100.5110.510
低频+高频0.5160.5160.5160.5170.516
中频+高频0.4950.4930.4930.4940.494
低频+中频+高频0.5240.5250.5250.5260.525

Table 3

Analysis for modal center selection"

实验名称Set1Set2Set3Set4平均
低频为中心0.5240.5250.5250.5260.525
中频为中心0.5120.5140.5130.5130.513
高频为中心0.4940.4920.4930.4930.493

Table 4

Comparison analysis of semantic modeling"

实验名称Set1Set2Set3Set4平均
基线模型0.4870.4870.4880.4870.487
LSTM建模0.5050.5070.5050.5060.506
MSPNet0.5240.5250.5250.5260.525

Fig.4

Performance comparison of various prediction models"

1 Dhar S, Ordonez V, Berg T L. High level describable attributes for predicting aesthetics and interestingness[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Colorado, USA, 2011: 1657-1664.
2 Lu X, Lin Z, Jin H, et al. Rating image aesthetics using deep learning[J]. IEEE Transactions on Multimedia, 2015, 17(11): 2021-2034.
3 Deng Y, Loy C C, Tang X. Image aesthetic assessment: an experimental survey[J]. IEEE Signal Processing Magazine, 2017, 34(4): 80-106.
4 Gygli M, Soleymani M. Analyzing and predicting GIF interestingness[C]∥Proceedings of the 24th ACM International Conference on Multimedia, New York, USA, 2016: 122-126.
5 Demarty C H, Sjöberg M, Ionescu B, et al. Mediaeval 2017 predicting media interestingness task[C]∥MediaEval Workshop, Dublin, Ireland, 2017: 1-3.
6 Khosla A, Das Sarma A, Hamid R. What makes an image popular?[C]∥Proceedings of the 23rd International Conference on World Wide Web, New York, USA, 2014: 867-876.
7 Deza A, Parikh D. Understanding image virality[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1818-1826.
8 Isola P, Xiao J, Parikh D, et al. What makes a photograph memorable?[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 36(7): 1469-1482.
9 Cohendet R, Demarty C H, Duong N Q K, et al. VideoMem: constructing, analyzing, predicting short-term and long-term video memorability[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea(South), 2019: 2531-2540.
10 Brady T F, Konkle T, Alvarez G A, et al. Visual long-term memory has a massive storage capacity for object details[J]. Proceedings of the National Academy of Sciences, 2008, 105(38): 14325-14329.
11 Khosla A, Raju A S, Torralba A, et al. Understanding and predicting image memorability at a large scale[C]∥Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 2390-2398.
12 Shekhar S, Singal D, Singh H, et al. Show and recall: Learning what makes videos memorable[C]∥Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy, 2017: 2730-2739.
13 Cohendet R, Yadati K, Duong N Q K, et al. Annotating, understanding, and predicting long-term video memorability[C]∥Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, New York, USA, 2018: 178-186.
14 Borth D, Chen T, Ji R, et al. Sentibank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content[C]∥Proceedings of the 21st ACM International Conference on Multimedia, Barcelona, Spain, 2013: 459-460.
15 Hara K, Kataoka H, Satoh Y. Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 6546-6555.
16 王小玉, 胡鑫豪, 韩昌林. 基于生成对抗网络的人脸铅笔画算法[J]. 吉林大学学报: 工学版, 2021, 51(1): 285-292.
Wang Xiao-yu, Hu Xin-hao, Han Chang-lin. Face pencil drawing algorithms based on generative adversarial network[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(1): 285-292.
17 Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]∥Advances in Neural Information Processing Systems, Red Hook, USA, 2017: 5998-6008.
18 Yang C, Xu Y, Shi J, et al. Temporal pyramid network for action recognition[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 591-600.
19 Zhou R, Shen Y D. End-to-end adversarial-attention network for multi-modal clustering[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 14619-14628.
20 Fajtl J, Argyriou V, Monekosso D, et al. Amnet: Memorability estimation with attention[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 6363-6372.
21 Giryes R, Sapiro G, Bronstein A M. Deep neural networks with random Gaussian weights: a universal classification strategy?[J]. IEEE Transactions on Signal Processing, 2016, 64(13): 3444-3457.
22 郭晓然, 罗平, 王维兰. 基于Transformer编码器的中文命名实体识别[J]. 吉林大学学报: 工学版, 2021, 51(3): 989-995.
Guo Xiao-ran, Luo Ping, Wang Wei-lan. Chinese named entity recognition based on Transformer encoder[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(3): 989-995.
23 Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale[J/OL]. [2020-10-22]. arXiv preprint arXiv:.
24 Chen H, Wang Y, Guo T, et al. Pre-trained image processing transformer[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 12299-12310.
25 Touvron H, Cord M, Douze M, et al. Training data-efficient image transformers & distillation through attention[C]∥International Conference on Machine Learning, Online, 2021: 10347-10357.
26 Chen Y, Li J, Xiao H, et al. Dual path networks[J/OL].[2021-11-12]. arXiv preprint arXiv:.
27 Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
28 Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems, Cambridge, USA, 2014: 2672-2680.
29 Greff K, Srivastava R K, Koutník J, et al. LSTM: A search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(10): 2222-2232.
30 Smola A J, Schölkopf B. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3): 199-222.
31 Fonarow G C, Adams K F, Abraham W T, et al. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis[J]. JAMA, 2005, 293(5): 572-580.
32 Prasad A M, Iverson L R, Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction[J]. Ecosystems, 2006, 9(2): 181-199.
33 Chen T, Guestrin C. XGBoost: a scalable tree boosting system[C]∥Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 2016: 785-794.
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