skip to main content
research-article

Bottom-up and Top-down Object Inference Networks for Image Captioning

Published:16 March 2023Publication History
Skip Abstract Section

Abstract

A bottom-up and top-down attention mechanism has led to the revolutionizing of image captioning techniques, which enables object-level attention for multi-step reasoning over all the detected objects. However, when humans describe an image, they often apply their own subjective experience to focus on only a few salient objects that are worthy of mention, rather than all objects in this image. The focused objects are further allocated in linguistic order, yielding the “object sequence of interest” to compose an enriched description. In this work, we present the Bottom-up and Top-down Object inference Network (BTO-Net), which novelly exploits the object sequence of interest as top-down signals to guide image captioning. Technically, conditioned on the bottom-up signals (all detected objects), an LSTM-based object inference module is first learned to produce the object sequence of interest, which acts as the top-down prior to mimic the subjective experience of humans. Next, both of the bottom-up and top-down signals are dynamically integrated via an attention mechanism for sentence generation. Furthermore, to prevent the cacophony of intermixed cross-modal signals, a contrastive learning-based objective is involved to restrict the interaction between bottom-up and top-down signals, and thus leads to reliable and explainable cross-modal reasoning. Our BTO-Net obtains competitive performances on the COCO benchmark, in particular, 134.1% CIDEr on the COCO Karpathy test split. Source code is available at https://github.com/YehLi/BTO-Net.

REFERENCES

  1. [1] Anderson Peter, Fernando Basura, Johnson Mark, and Gould Stephen. 2016. Spice: Semantic propositional image caption evaluation. In European Conference on Computer Vision. Springer, 382398.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Anderson Peter, He Xiaodong, Buehler Chris, Teney Damien, Johnson Mark, Gould Stephen, and Zhang Lei. 2018. Bottom-up and top-down attention for image captioning and visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 60776086.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Bahdanau Dzmitry, Cho Kyung Hyun, and Bengio Yoshua. 2015. Neural machine translation by jointly learning to align and translate. In 3rd International Conference on Learning Representations (ICLR’15).Google ScholarGoogle Scholar
  4. [4] Banerjee Satanjeev and Lavie Alon. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 6572.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Ben Huixia, Pan Yingwei, Li Yehao, Yao Ting, Hong Richang, Wang Meng, and Mei Tao. 2021. Unpaired image captioning with semantic-constrained self-learning. IEEE Transactions on Multimedia 24 (2021), 904–916.Google ScholarGoogle Scholar
  6. [6] Chen Shizhe, Jin Qin, Wang Peng, and Wu Qi. 2020. Say as you wish: Fine-grained control of image caption generation with abstract scene graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 99629971.Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Cornia Marcella, Baraldi Lorenzo, and Cucchiara Rita. 2019. Show, control and tell: A framework for generating controllable and grounded captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 83078316.Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Cornia Marcella, Baraldi Lorenzo, Serra Giuseppe, and Cucchiara Rita. 2018. Paying more attention to saliency: Image captioning with saliency and context attention. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 2 (2018), 121.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. [9] Cornia Marcella, Stefanini Matteo, Baraldi Lorenzo, and Cucchiara Rita. 2020. Meshed-memory transformer for image captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1057810587.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Devlin Jacob, Cheng Hao, Fang Hao, Gupta Saurabh, Deng Li, He Xiaodong, Zweig Geoffrey, and Mitchell Margaret. 2015. Language models for image captioning: The quirks and what works. In 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP’15). Association for Computational Linguistics (ACL), 100105.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Ding Yang, Yu Jing, Liu Bang, Hu Yue, Cui Mingxin, and Wu Qi. 2022. MuKEA: Multimodal knowledge extraction and accumulation for knowledge-based visual question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 50895098.Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Fang Hao, Gupta Saurabh, Iandola Forrest, Srivastava Rupesh K., Deng Li, Dollár Piotr, Gao Jianfeng, He Xiaodong, Mitchell Margaret, Platt John C., et al. 2015. From captions to visual concepts and back. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 14731482.Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Farhadi Ali, Hejrati Mohsen, Sadeghi Mohammad Amin, Young Peter, Rashtchian Cyrus, Hockenmaier Julia, and Forsyth David. 2010. Every picture tells a story: Generating sentences from images. In European Conference on Computer Vision. Springer, 1529.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. [14] He Chen and Hu Haifeng. 2019. Image captioning with visual-semantic double attention. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 15, 1 (2019), 116.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Herdade Simao, Kappeler Armin, Boakye Kofi, and Soares Joao. 2019. Image captioning: Transforming objects into words. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 11137–11147.Google ScholarGoogle Scholar
  16. [16] Hou Jingyi, Wu Xinxiao, Zhang Xiaoxun, Qi Yayun, Jia Yunde, and Luo Jiebo. 2020. Joint commonsense and relation reasoning for image and video captioning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1097310980.Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Huang Lun, Wang Wenmin, Chen Jie, and Wei Xiao-Yong. 2019. Attention on attention for image captioning. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 46344643.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Jiang Wenhao, Ma Lin, Jiang Yu-Gang, Liu Wei, and Zhang Tong. 2018. Recurrent fusion network for image captioning. In Proceedings of the European Conference on Computer Vision (ECCV’18). 499515.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Jiang Weitao, Wang Weixuan, and Hu Haifeng. 2021. Bi-directional co-attention network for image captioning. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 17, 4 (2021), 120.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Jiang Xiaoze, Du Siyi, Qin Zengchang, Sun Yajing, and Yu Jing. 2020. Kbgn: Knowledge-bridge graph network for adaptive vision-text reasoning in visual dialogue. In Proceedings of the 28th ACM International Conference on Multimedia. 12651273.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. [21] Karpathy Andrej and Fei-Fei Li. 2015. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 31283137.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Kenton Jacob Devlin Ming-Wei Chang and Toutanova Lee Kristina. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT. 41714186.Google ScholarGoogle Scholar
  23. [23] Kingma Diederik and Ba Jimmy. 2015. Adam: A method for stochastic optimization. In ICLR.Google ScholarGoogle Scholar
  24. [24] Krishna Ranjay, Zhu Yuke, Groth Oliver, Johnson Justin, Hata Kenji, Kravitz Joshua, Chen Stephanie, Kalantidis Yannis, Li Li-Jia, Shamma David A., et al. 2017. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International Journal of Computer Vision 123, 1 (2017), 3273.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. [25] Kulkarni Girish, Premraj Visruth, Ordonez Vicente, Dhar Sagnik, Li Siming, Choi Yejin, Berg Alexander C., and Berg Tamara L.. 2013. Babytalk: Understanding and generating simple image descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 12 (2013), 28912903.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. [26] Li Xiangyang and Jiang Shuqiang. 2019. Know more say less: Image captioning based on scene graphs. IEEE Transactions on Multimedia 21, 8 (2019), 21172130.Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Li Xiujun, Yin Xi, Li Chunyuan, Zhang Pengchuan, Hu Xiaowei, Zhang Lei, Wang Lijuan, Hu Houdong, Dong Li, Wei Furu, et al. 2020. Oscar: Object-semantics aligned pre-training for vision-language tasks. In European Conference on Computer Vision. Springer, 121137.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. [28] Li Yehao, Pan Yingwei, Chen Jingwen, Yao Ting, and Mei Tao. 2021. X-modaler: A versatile and high-performance codebase for cross-modal analytics. In Proceedings of the 29th ACM International Conference on Multimedia. 37993802.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Li Yehao, Pan Yingwei, Yao Ting, Chen Jingwen, and Mei Tao. 2021. Scheduled sampling in vision-language pretraining with decoupled encoder-decoder network. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 85188526.Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Li Yehao, Pan Yingwei, Yao Ting, and Mei Tao. 2022. Comprehending and ordering semantics for image captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1799017999.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Li Yehao, Yao Ting, Pan Yingwei, and Mei Tao. 2022. Contextual transformer networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2022), 1489–1500.Google ScholarGoogle Scholar
  32. [32] Lin Chin-Yew. 2004. Rouge: A package for automatic evaluation of summaries. In ACL Workshop.Google ScholarGoogle Scholar
  33. [33] Lin Tsung-Yi, Maire Michael, Belongie Serge, Hays James, Perona Pietro, Ramanan Deva, Dollár Piotr, and Zitnick C. Lawrence. 2014. Microsoft COCO: Common objects in context. In European Conference on Computer Vision. Springer, 740755.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Lu Jiasen, Xiong Caiming, Parikh Devi, and Socher Richard. 2017. Knowing when to look: Adaptive attention via a visual sentinel for image captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 375383.Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Lu Jiasen, Yang Jianwei, Batra Dhruv, and Parikh Devi. 2018. Neural baby talk. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 72197228.Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Luo Jianjie, Li Yehao, Pan Yingwei, Yao Ting, Chao Hongyang, and Mei Tao. 2021. CoCo-BERT: Improving video-language pre-training with contrastive cross-modal matching and denoising. In Proceedings of the 29th ACM International Conference on Multimedia. 56005608.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. [37] Luo Jianjie, Li Yehao, Pan Yingwei, Yao Ting, Feng Jianlin, Chao Hongyang, and Mei Tao. 2022. Semantic-conditional diffusion networks for image captioning. arXiv preprint arXiv:2212.03099 (2022).Google ScholarGoogle Scholar
  38. [38] Mao Junhua, Xu Wei, Yang Yi, Wang Jiang, and Yuille Alan L.. 2014. Explain images with multimodal recurrent neural networks. In NIPS Workshop on Deep Learning.Google ScholarGoogle Scholar
  39. [39] Oord Aaron van den, Li Yazhe, and Vinyals Oriol. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).Google ScholarGoogle Scholar
  40. [40] Pan Yingwei, Li Yehao, Luo Jianjie, Xu Jun, Yao Ting, and Mei Tao. 2022. Auto-captions on GIF: A large-scale video-sentence dataset for vision-language pre-training. In Proceedings of the 30th ACM International Conference on Multimedia. 70707074.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. [41] Pan Yingwei, Yao Ting, Li Yehao, and Mei Tao. 2020. X-linear attention networks for image captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1097110980.Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Papineni Kishore, Roukos Salim, Ward Todd, and Zhu Wei-Jing. 2002. Bleu: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. 311318.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. [43] Qin Yu, Du Jiajun, Zhang Yonghua, and Lu Hongtao. 2019. Look back and predict forward in image captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 83678375.Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Rennie Steven J., Marcheret Etienne, Mroueh Youssef, Ross Jerret, and Goel Vaibhava. 2017. Self-critical sequence training for image captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 70087024.Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Rohrbach Anna, Hendricks Lisa Anne, Burns Kaylee, Darrell Trevor, and Saenko Kate. 2018. Object hallucination in image captioning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 40354045.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Sharma Piyush, Ding Nan, Goodman Sebastian, and Soricut Radu. 2018. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 25562565.Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Shi Zhan, Liu Hui, and Zhu Xiaodan. 2021. Enhancing descriptive image captioning with natural language inference. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 269277.Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Shi Zhan, Zhou Xu, Qiu Xipeng, and Zhu Xiaodan. 2020. Improving image captioning with better use of caption. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 74547464.Google ScholarGoogle ScholarCross RefCross Ref
  49. [49] Sutskever Ilya, Vinyals Oriol, and Le Quoc V.. 2014. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems. 31043112.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. [50] Toutanova Kristina, Klein Dan, Manning Christopher D., and Singer Yoram. 2003. Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. 252259.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. [51] Vaswani Ashish, Shazeer Noam, Parmar Niki, Uszkoreit Jakob, Jones Llion, Gomez Aidan N., Kaiser Łukasz, and Polosukhin Illia. 2017. Attention is all you need. In NeurIPS.Google ScholarGoogle Scholar
  52. [52] Vedantam Ramakrishna, Zitnick C. Lawrence, and Parikh Devi. 2015. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 45664575.Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Vinyals Oriol, Toshev Alexander, Bengio Samy, and Erhan Dumitru. 2015. Show and tell: A neural image caption generator. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 31563164.Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Wang Cheng, Yang Haojin, and Meinel Christoph. 2018. Image captioning with deep bidirectional LSTMs and multi-task learning. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 2s (2018), 120.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. [55] Wang Jing, Pan Yingwei, Yao Ting, Tang Jinhui, and Mei Tao. 2019. Convolutional auto-encoding of sentence topics for image paragraph generation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 940946.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. [56] Wei Haiyang, Li Zhixin, Huang Feicheng, Zhang Canlong, Ma Huifang, and Shi Zhongzhi. 2021. Integrating scene semantic knowledge into image captioning. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 17, 2 (2021), 122.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. [57] Wu Yonghui, Schuster Mike, Chen Zhifeng, Le Quoc V., Norouzi Mohammad, Macherey Wolfgang, Krikun Maxim, Cao Yuan, Gao Qin, Macherey Klaus, et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016).Google ScholarGoogle Scholar
  58. [58] Xu Kelvin, Ba Jimmy, Kiros Ryan, Cho Kyunghyun, Courville Aaron, Salakhudinov Ruslan, Zemel Rich, and Bengio Yoshua. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning. PMLR, 20482057.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. [59] Xu Ning, Zhang Hanwang, Liu An-An, Nie Weizhi, Su Yuting, Nie Jie, and Zhang Yongdong. 2019. Multi-level policy and reward-based deep reinforcement learning framework for image captioning. IEEE Transactions on Multimedia 22, 5 (2019), 13721383.Google ScholarGoogle ScholarCross RefCross Ref
  60. [60] Yang Xu, Gao Chongyang, Zhang Hanwang, and Cai Jianfei. 2021. Auto-parsing network for image captioning and visual question answering. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 21972207.Google ScholarGoogle ScholarCross RefCross Ref
  61. [61] Yang Xu, Tang Kaihua, Zhang Hanwang, and Cai Jianfei. 2019. Auto-encoding scene graphs for image captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1068510694.Google ScholarGoogle ScholarCross RefCross Ref
  62. [62] Yao Ting, Li Yehao, Pan Yingwei, Wang Yu, Zhang Xiao-Ping, and Mei Tao. 2022. Dual vision transformer. arXiv preprint arXiv:2207.04976 (2022).Google ScholarGoogle Scholar
  63. [63] Yao Ting, Pan Yingwei, Li Yehao, and Mei Tao. 2018. Exploring visual relationship for image captioning. In Proceedings of the European Conference on Computer Vision (ECCV’18). 684699.Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. [64] Yao Ting, Pan Yingwei, Li Yehao, and Mei Tao. 2019. Hierarchy parsing for image captioning. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 26212629.Google ScholarGoogle ScholarCross RefCross Ref
  65. [65] Yao Ting, Pan Yingwei, Li Yehao, Ngo Chong-Wah, and Mei Tao. 2022. Wave-vit: Unifying wavelet and transformers for visual representation learning. In European Conference on Computer Vision. Springer, 328345.Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. [66] Yao Ting, Pan Yingwei, Li Yehao, Qiu Zhaofan, and Mei Tao. 2017. Boosting image captioning with attributes. In Proceedings of the IEEE International Conference on Computer Vision. 48944902.Google ScholarGoogle ScholarCross RefCross Ref
  67. [67] You Quanzeng, Jin Hailin, Wang Zhaowen, Fang Chen, and Luo Jiebo. 2016. Image captioning with semantic attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 46514659.Google ScholarGoogle ScholarCross RefCross Ref
  68. [68] Yu Jing, Zhu Zihao, Wang Yujing, Zhang Weifeng, Hu Yue, and Tan Jianlong. 2020. Cross-modal knowledge reasoning for knowledge-based visual question answering. Pattern Recognition 108 (2020), 107563.Google ScholarGoogle ScholarCross RefCross Ref
  69. [69] Zhou Luowei, Palangi Hamid, Zhang Lei, Hu Houdong, Corso Jason, and Gao Jianfeng. 2020. Unified vision-language pre-training for image captioning and VQA. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1304113049.Google ScholarGoogle ScholarCross RefCross Ref
  70. [70] Zhou Yimin, Sun Yiwei, and Honavar Vasant. 2019. Improving image captioning by leveraging knowledge graphs. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV’19). IEEE, 283293.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Bottom-up and Top-down Object Inference Networks for Image Captioning

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 5
          September 2023
          262 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3585398
          • Editor:
          • Abdulmotaleb El Saddik
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 16 March 2023
          • Online AM: 19 January 2023
          • Accepted: 3 January 2023
          • Revised: 14 December 2022
          • Received: 13 August 2022
          Published in tomm Volume 19, Issue 5

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
        • Article Metrics

          • Downloads (Last 12 months)297
          • Downloads (Last 6 weeks)28

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text

        HTML Format

        View this article in HTML Format .

        View HTML Format
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!