Akhtar, Z., Ofli, F., and Imran, M. (2021). “Towards Using Remote Sensing and Social Media Data for Flood
Mapping”. In: 18th International Conference on Information Systems for Crisis Response and Management,
ISCRAM 2021, Blacksburg, VA, USA, May 2021. Ed. by A. Adrot, R. Grace, K. A. Moore, and C. W. Zobel.
ISCRAM Digital Library, pp. 536–551.
Alam, F., Imran, M., and Ofli, F. (2017). “Image4act: Online social media image processing for disaster response”.
In: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and
mining 2017, pp. 601–604.
Alam, F., Ofli, F., and Imran, M. (2018). “CrisisMMD: Multimodal Twitter Datasets from Natural Disasters”.
In: Proceedings of the Twelfth International Conference on Web and Social Media, ICWSM 2018, Stanford,
California, USA, June 25-28, 2018. AAAI Press, pp. 465–473.
Asami, K., Fujita, S., Hiroi, K., and Hatayama, M. (2022). “Data Augmentation with Synthesized Damaged Roof
Images Generated by GAN”. In: 19th International Conference on Information Systems for Crisis Response and
Management, ISCRAM 2022, Tarbes, France, May 22-25, 2022. Ed. by R. Grace and H. Baharmand. ISCRAM
Digital Library, pp. 256–265.
Baevski, A., Hsu, W., Xu, Q., Babu, A., Gu, J., and Auli, M. (2022). “data2vec: A General Framework for
Self-supervised Learning in Speech, Vision and Language”. In: International Conference on Machine Learning,
ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. Ed. by K. Chaudhuri, S. Jegelka, L. Song, C. Szepesv ́ari,
G. Niu, and S. Sabato. Vol. 162. Proceedings of Machine Learning Research. PMLR, pp. 1298–1312.
Buntain, C., McCreadie, R., and Soboroff, I. (2022). “Incident Streams 2021 Off the Deep End: Deeper Annotations
and Evaluations in Twitter”. In: 19th International Conference on Information Systems for Crisis Response and
Management, ISCRAM 2022, Tarbes, France, May 22-25, 2022. Ed. by R. Grace and H. Baharmand. ISCRAM
Digital Library, pp. 584–604.
Daly, S. and Thom, J. A. (2016). “Mining and Classifying Image Posts on Social Media to Analyse Fires”. In:
13th Proceedings of the International Conference on Information Systems for Crisis Response and Management,
Rio de Janeiro, Brasil, May 22-25, 2016. Ed. by A. H. Tapia, P. Antunes, V. A. Ba ̃nuls, K. A. Moore, and
J. P. de Albuquerque. ISCRAM Association.
Deng, J., Dong, W., Socher, R., Li, L.- J., Li, K., and Fei-Fei, L. (2009). “ImageNet: A Large-Scale Hierarchical
Image Database”. In: CVPR09.
Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2019). “BERT: Pre-training of Deep Bidirectional Transformers
for Language Understanding”. In: Proceedings of the 2019 Conference of the North American Chapter of the
Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis,
MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers). Ed. by J. Burstein, C. Doran, and T. Solorio.
Association for Computational Linguistics, pp. 4171–4186.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M.,
Heigold, G., and Gelly, S. (2020). “An image is worth 16x16 words: Transformers for image recognition at scale”.
In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021.
Gao, P., Jiang, Z., You, H., Lu, P., Hoi, S. C. H., Wang, X., and Li, H. (2019). “Dynamic Fusion With Intra- and
Inter-Modality Attention Flow for Visual Question Answering”. In: IEEE Conference on Computer Vision and
Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019. Computer Vision Foundation / IEEE,
pp. 6639–6648.
Girshick, R. B., Donahue, J., Darrell, T., and Malik, J. (2014). “Rich Feature Hierarchies for Accurate Object
Detection and Semantic Segmentation”. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition,
CVPR 2014, Columbus, OH, USA, June 23-28, 2014. IEEE Computer Society, pp. 580–587.
He, K., Chen, X., Xie, S., Li, Y., Doll ́ar, P., and Girshick, R. B. (2022). “Masked Autoencoders Are Scalable Vision
Learners”. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans,
LA, USA, June 18-24, 2022. IEEE, pp. 15979–15988.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). “Deep Residual Learning for Image Recognition”. In: 2016 IEEE
Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016.
IEEE Computer Society, pp. 770–778.
Imran, M., Alam, F., Qazi, U., Peterson, S., and Ofli, F. (2020). “Rapid Damage Assessment Using Social Media
Images by Combining Human and Machine Intelligence”. In: 17th International Conference on Information
Systems for Crisis Response and Management, ISCRAM 2020, May 2020. Ed. by A. L. Hughes, F. McNeill, and
C. W. Zobel. ISCRAM Digital Library, pp. 761–773.
Imran, M., Castillo, C., Diaz, F., and Vieweg, S. (2015). “Processing Social Media Messages in Mass Emergency: A
Survey”. In: ACM Comput. Surv. 47.4, 67:1–67:38.
Imran, M., Castillo, C., Lucas, J., Meier, P., and Vieweg, S. (2014). “AIDR: Artificial intelligence for disaster
response”. In: Proceedings of WWW. ACM.
Kumar, S., Barbier, G., Abbasi, M. A., and Liu, H. (2011). “TweetTracker: An Analysis Tool for Humanitarian and
Disaster Relief”. In: Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona,
Catalonia, Spain, July 17-21, 2011. The AAAI Press.
Li, X. and Caragea, D. (2020). “Improving Disaster-related Tweet Classification with a Multimodal Approach”. In:
17th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2020,
May 2020. Ed. by A. L. Hughes, F. McNeill, and C. W. Zobel. ISCRAM Digital Library, pp. 893–902.
Li, X., Caragea, D., Caragea, C., Imran, M., and Ofli, F. (2019). “Identifying Disaster Damage Images Using a
Domain Adaptation Approach”. In: Proceedings of the 16th International Conference on Information Systems
for Crisis Response and Management, Val`encia, Spain, May 19-22, 2019. Ed. by Z. Franco, J. J. Gonz ́alez, and
J. H. Can ́os. ISCRAM Association.
McCreadie, R., Buntain, C., and Soboroff, I. (2020). “Incident Streams 2019: Actionable Insights and How to Find
Them”. In: 17th International Conference on Information Systems for Crisis Response and Management, ISCRAM
2020, May 2020. Ed. by A. L. Hughes, F. McNeill, and C. W. Zobel. ISCRAM Digital Library, pp. 744–760.
Mouzannar, H., Rizk, Y., and Awad, M. (2018). “Damage Identification in Social Media Posts using Multimodal
Deep Learning”. In: Proceedings of the 15th International Conference on Information Systems for Crisis Response
and Management, Rochester, NY, USA, May 20-23, 2018. Ed. by K. Boersma and B. M. Tomaszewski. ISCRAM
Association.
Nguyen, D. T., Ofli, F., Imran, M., and Mitra, P. (2017). “Damage assessment from social media imagery data during
disasters”. In: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks
analysis and mining 2017, pp. 569–576.
Nguyen, D. T., Joty, S. R., Imran, M., Sajjad, H., and Mitra, P. (2016). “Applications of Online Deep Learning for
Crisis Response Using Social Media Information”. In: CoRR abs/1610.01030. arXiv: 1610.01030.
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., and Tran, D. (2018). “Image transformer”.
In: International Conference on Machine Learning. PMLR, pp. 4055–4064.
Redmon, J., Divvala, S. K., Girshick, R. B., and Farhadi, A. (2016). “You Only Look Once: Unified, Real-Time
Object Detection”. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las
Vegas, NV, USA, June 27-30, 2016. IEEE Computer Society, pp. 779–788.
Ren, S., He, K., Girshick, R. B., and Sun, J. (2017). “Faster R-CNN: Towards Real-Time Object Detection with
Region Proposal Networks”. In: IEEE Trans. Pattern Anal. Mach. Intell. 39.6, pp. 1137–1149.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., and
Bernstein, M. (2015). “Imagenet large scale visual recognition challenge”. In: International journal of computer
vision 115.3, pp. 211–252.
Said, N., Ahmad, K., Riegler, M., Pogorelov, K., Hassan, L., Ahmad, N., and Conci, N. (2019). “Natural disasters
detection in social media and satellite imagery: a survey”. In: Multimedia Tools and Applications 78.22,
pp. 31267–31302.
Shekhar, H. and Setty, S. (2015). “Disaster analysis through tweets”. In: 2015 International Conference on Advances
in Computing, Communications and Informatics (ICACCI). IEEE, pp. 1719–1723.
Simonyan, K. and Zisserman, A. (2015). “Very Deep Convolutional Networks for Large-Scale Image Recognition”.
In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015,
Conference Track Proceedings. Ed. by Y. Bengio and Y. LeCun.
Sosea, T., Sirbu, I., Caragea, C., Caragea, D., and Rebedea, T. (2021). “Using the Image-Text Relationship to
Improve Multimodal Disaster Tweet Classification”. In: 18th International Conference on Information Systems for
Crisis Response and Management, ISCRAM 2021, Blacksburg, VA, USA, May 2021. Ed. by A. Adrot, R. Grace,
K. A. Moore, and C. W. Zobel. ISCRAM Digital Library, pp. 691–704.
Tan, M. and Le, Q. V. (2019). “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”. In:
Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long
Beach, California, USA. Ed. by K. Chaudhuri and R. Salakhutdinov. Vol. 97. Proceedings of Machine Learning
Research. PMLR, pp. 6105–6114.
To, H., Agrawal, S., Kim, S. H., and Shahabi, C. (2017). “On identifying disaster-related tweets: Matching-based
or learning-based?” In: 2017 IEEE third international conference on multimedia big data (BigMM). IEEE,
pp. 330–337.
Torrey, L. and Shavlik, J. (2010). “Transfer learning”. In: Handbook of research on machine learning applications
and trends: algorithms, methods, and techniques. IGI global, pp. 242–264.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017).
“Attention is All you Need”. In: Advances in Neural Information Processing Systems 30: Annual Conference on
Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. Ed. by I. Guyon,
U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, pp. 5998–6008.
Weber, E., Papadopoulos, D. P., Lapedriza, `A., Ofli, F., Imran, M., and Torralba, A. (2022). “Incidents1M: a
large-scale dataset of images with natural disasters, damage, and incidents”. In: CoRR abs/2201.04236. arXiv:
2201.04236.
Weissenborn, D., T ̈ackstr ̈om, O., and Uszkoreit, J. (2020). “Scaling Autoregressive Video Models”. In: 8th
International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020.
Widener, M. J. and Li, W. (2014). “Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy
food references across the US”. In: Applied Geography 54, pp. 189–197.
Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., and Hu, H. (2022). “Simmim: A simple framework
for masked image modeling”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition, pp. 9653–9663.
Yin, J., Karimi, S., Lampert, A., Cameron, M. A., Robinson, B., and Power, R. (2015). “Using Social Media to
Enhance Emergency Situation Awareness: Extended Abstract”. In: Proceedings of the Twenty-Fourth International
Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015. AAAI Press,
pp. 4234–4239.
Zhou, H.- Y., Lu, C., Yang, S., and Yu, Y. (2021). “ConvNets vs. Transformers: Whose visual representations are more
transferable?” In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2230–2238.
![[img]](https://eprints.gla.ac.uk/style/images/fileicons/text.png)
References
References