ARCHIVES
Review Article
Position Bias in Multimodal Disaster-Response Systems: A Systematic Review and Research Gap Analysis
Jiya Sharma1
Anil Kumar Mishra2
1 2 Department of Computer Science, Amity University, Haryana Gurugram, India.
Published Online: May-August 2026
Pages: 203-215
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502022References
1. A.R. e. al, “Learning transferable visual models from natural language supervision.,” International conference on machine learning, pp.
8748-8763, 2021.
2. J. D. B. D. P. a. S. L. Lu, “Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.,” Advances in
neural information processing systems , p. 32, 2019.
3. C.Y. Y. Y. X. Y.-T. C. Z. P. H. P. Q. L. Y.-H. S. Z. L. a. T. D. Jia, “Scaling up visual and vision-language representation learning with noisy
text supervision.,” International conference on machine learning., pp. 4904-4916, 2021.
4. X. L. a. F. L. H. Li, “Multimodal deep learning for disaster response: A survey,” IEEE Access, vol. vol. 8 , p. 181365–181382, 2020.
5. M. M. P. a. C. C. Imran, “Twitter as a lifeline: Human-annotated twitter corpora for NLP of crisis-related messages.,” Proceedings of the
Tenth International Conference on Language Resources and Evaluation (LREC'16), pp. 1638-1643, 2016.
6. T. C. A. a. L.-P. M. Baltrušaitis, “Multimodal machine learning: A survey and taxonomy,” IEEE transactions on pattern analysis and machine
intelligence 41, vol. 2, pp. 423-443., 2018.
7. X. Y. a. Y. W. D. Zhang, “Multimodal learning for disaster detection and classification: A review,” Information Fusion, vol. 62, pp. 80-93,
2021.
8. M. P. E. &. S. N. Hardt, “Equality of opportunity in supervised learning.,” Advances in neural information processing systems, vol. 29, 2016.
9. A,.S. N. P. N. U. J. J. L. G. A. K. Ł. a. P. I. Vaswani, “Attention is all you need.,” Advances in neural information processing systems, vol.
30, 2017.
10. J.C. M. W. L. K. &. T. K. Devlin, “Bert: Pre-training of deep bidirectional transformers for language understanding.,” Proceedings of the
2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol. 1, pp.
4171-4186, 2019.
11. S. K. a. A. S. H. Sharma, “Multimodal fusion techniques for disaster classification using deep learning,” IEEE Access, vol. 9, p. 123456–
123470, 2021.
12. R. K. G. a. P. K. Sharma, “Deep learning-based disaster detection using multimodal data,” Expert Syst. Appl., vol. 165, 2021.
13. F. I. M. &. O. F. Alam, “Image4act: Online social media image processing for disaster response,” Proceedings of the 2017 IEEE/ACM
international conference on advances in social networks analysis and mining 2017, pp. 601-604, 2017.
14. Z. L. a. X. C. Y. Wang, “Multimodal sentiment and event analysis: A survey,” ACM Comput. Surv., vol. 54, 2022.
15. S. K. M. N. a. G. H. T. Chen, “A simple framework for contrastive learning of visual representations,” Proc. Int. Conf. Machine Learning
(ICML), 2020.
16. H. F. Y. W. S. X. a. R. G. K. He, “Momentum contrast for unsupervised visual representation learning,” Proc. IEEE Conf. Comput. Vis.
Pattern Recognit. (CVPR), 2020.
17. S. H. M. &. N. A. Barocas, “Fairness and machine learning: Limitations and opportunities.,” MIT press., 2023.
18. B. L. K. A. W. D. Z. X. U. T. Dosovitskiy, “An image is worth 16x16 words: Transformers for image recognition at scale.,” arXiv preprint
arXiv:2010, 2020.
19. N. C. I. G. N. P. A. O. a. C. R. D. Berthelot, “MixMatch: A holistic approach to semi-supervised learning.,” Proc. Adv. Neural Inf. Process.
Syst. (NeurIPS), 2019.
20. T. a. H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning
results.,” Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2017.
21. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett, vol. 8, pp. 861-874, 2006.
22. R. Caruana, “Multitask learning,” Machine Learning, vol. 28, pp. 41-75, 1997
8748-8763, 2021.
2. J. D. B. D. P. a. S. L. Lu, “Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.,” Advances in
neural information processing systems , p. 32, 2019.
3. C.Y. Y. Y. X. Y.-T. C. Z. P. H. P. Q. L. Y.-H. S. Z. L. a. T. D. Jia, “Scaling up visual and vision-language representation learning with noisy
text supervision.,” International conference on machine learning., pp. 4904-4916, 2021.
4. X. L. a. F. L. H. Li, “Multimodal deep learning for disaster response: A survey,” IEEE Access, vol. vol. 8 , p. 181365–181382, 2020.
5. M. M. P. a. C. C. Imran, “Twitter as a lifeline: Human-annotated twitter corpora for NLP of crisis-related messages.,” Proceedings of the
Tenth International Conference on Language Resources and Evaluation (LREC'16), pp. 1638-1643, 2016.
6. T. C. A. a. L.-P. M. Baltrušaitis, “Multimodal machine learning: A survey and taxonomy,” IEEE transactions on pattern analysis and machine
intelligence 41, vol. 2, pp. 423-443., 2018.
7. X. Y. a. Y. W. D. Zhang, “Multimodal learning for disaster detection and classification: A review,” Information Fusion, vol. 62, pp. 80-93,
2021.
8. M. P. E. &. S. N. Hardt, “Equality of opportunity in supervised learning.,” Advances in neural information processing systems, vol. 29, 2016.
9. A,.S. N. P. N. U. J. J. L. G. A. K. Ł. a. P. I. Vaswani, “Attention is all you need.,” Advances in neural information processing systems, vol.
30, 2017.
10. J.C. M. W. L. K. &. T. K. Devlin, “Bert: Pre-training of deep bidirectional transformers for language understanding.,” Proceedings of the
2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol. 1, pp.
4171-4186, 2019.
11. S. K. a. A. S. H. Sharma, “Multimodal fusion techniques for disaster classification using deep learning,” IEEE Access, vol. 9, p. 123456–
123470, 2021.
12. R. K. G. a. P. K. Sharma, “Deep learning-based disaster detection using multimodal data,” Expert Syst. Appl., vol. 165, 2021.
13. F. I. M. &. O. F. Alam, “Image4act: Online social media image processing for disaster response,” Proceedings of the 2017 IEEE/ACM
international conference on advances in social networks analysis and mining 2017, pp. 601-604, 2017.
14. Z. L. a. X. C. Y. Wang, “Multimodal sentiment and event analysis: A survey,” ACM Comput. Surv., vol. 54, 2022.
15. S. K. M. N. a. G. H. T. Chen, “A simple framework for contrastive learning of visual representations,” Proc. Int. Conf. Machine Learning
(ICML), 2020.
16. H. F. Y. W. S. X. a. R. G. K. He, “Momentum contrast for unsupervised visual representation learning,” Proc. IEEE Conf. Comput. Vis.
Pattern Recognit. (CVPR), 2020.
17. S. H. M. &. N. A. Barocas, “Fairness and machine learning: Limitations and opportunities.,” MIT press., 2023.
18. B. L. K. A. W. D. Z. X. U. T. Dosovitskiy, “An image is worth 16x16 words: Transformers for image recognition at scale.,” arXiv preprint
arXiv:2010, 2020.
19. N. C. I. G. N. P. A. O. a. C. R. D. Berthelot, “MixMatch: A holistic approach to semi-supervised learning.,” Proc. Adv. Neural Inf. Process.
Syst. (NeurIPS), 2019.
20. T. a. H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning
results.,” Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2017.
21. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett, vol. 8, pp. 861-874, 2006.
22. R. Caruana, “Multitask learning,” Machine Learning, vol. 28, pp. 41-75, 1997
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