This bibliography contains a list of the papers that have been published using data from the Multimedia Evaluation Benchmark (MediaEval). It includes not only the papers from the proceedings of the yearly MediaEval workshop, but also conference and journal papers that have been published, as well as some theses. So far, we found 1012 papers that use data from MediaEval.

If you don’t see your paper here and would like to have it included, please get in touch with Mihai Gabriel Constantin: mihai.constantin84 (at) upb.ro.

• Azri, A., Favre, C., Harbi, N., Darmont, J., & Noûs, C. (2021). MONITOR: A Multimodal Fusion Framework to Assess Message Veracity in Social Networks. European Conference on Advances in Databases and Information Systems, 73–87.
• Mahalle, M. D., & Rojatkar, D. V. (2021). Audio Based Violent Scene Detection Using Extreme Learning Machine Algorithm. 2021 6th International Conference for Convergence in Technology (I2CT), 1–8.
• Xue, J., Wang, Y., Tian, Y., Li, Y., Shi, L., & Wei, L. (2021). Detecting fake news by exploring the consistency of multimodal data. Information Processing & Management, 58(5), 102610.
• Aktas, K., Demirel, M., Moor, M., Olesk, J., Ozcinar, C., & Anbarjafari, G. (2021). Spatiotemporal based table tennis stroke-type assessment. Signal, Image and Video Processing, 1–8.
• Dharmapriya, J., Dayarathne, L., Diasena, T., Arunathilake, S., Kodikara, N., & Wijesekera, P. (2021). Music Emotion Visualization through Colour. 2021 International Conference on Electronics, Information, and Communication (ICEIC), 1–6.
• Shu, Y., Li, Q., Xu, C., Liu, S., & Xu, G. (2021). V-SVR+: Support Vector Regression with Variational Privileged Information. IEEE Transactions on Multimedia.
• Constantin, M. G., Ştefan, L.-D., Ionescu, B., Duong, N. Q. K., Demarty, C.-H., & Sjöberg, M. (2021). Visual Interestingness Prediction: A Benchmark Framework and Literature Review. International Journal of Computer Vision, 1–25.
• Jha, D., Ali, S., Hicks, S., Thambawita, V., Borgli, H., Smedsrud, P. H., de Lange, T., Pogorelov, K., Wang, X., Harzig, P., & others. (2021). A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging. Medical Image Analysis, 70, 102007.
• Khadka, R. (2021). Meta-Learning for Medical Image Segmentation [PhD thesis]. University OF Trieste.
• Somandepalli, K., Guha, T., Martinez, V. R., Kumar, N., Adam, H., & Narayanan, S. (2021). Computational media intelligence: human-centered machine analysis of media. Proceedings of the IEEE.
• Tomar, N. K., Jha, D., Ali, S., Johansen, H. D., Johansen, D., Riegler, M. A., & Halvorsen, P. (2021). DDANet: Dual decoder attention network for automatic polyp segmentation. International Conference on Pattern Recognition, 307–314.
• Kalakota, V., & Bansal, A. (2021). Diversifying Relevant Search Results from Social Media using Community Contributed Images. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 376–385.
• Thao, H. T. P., Balamurali, B. T., Herremans, D., & Roig, G. (2021). AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies. 2020 25th International Conference on Pattern Recognition (ICPR), 8719–8726.
• Hu, Y., Settle, S., & Livescu, K. (2021). Acoustic span embeddings for multilingual query-by-example search. 2021 IEEE Spoken Language Technology Workshop (SLT), 935–942.
• Constantin, M. G., Ştefan, L.-D., & Ionescu, B. (2021). DeepFusion: Deep Ensembles for Domain Independent System Fusion. International Conference on Multimedia Modeling, 240–252.
• da Fonseca, G. B., Sargent, G., Sicre, R., Patrocı́nio Zenilton KG, Gravier, G., & Guimaraes, S. J. F. (2021). Hierarchical multi-label propagation using speaking face graphs for multimodal person discovery. Multimedia Tools and Applications, 80(2), 2797–2820.
• Zhao, Y., & Guo, J. (2021). MusiCoder: A Universal Music-Acoustic Encoder Based on Transformer. International Conference on Multimedia Modeling, 417–429.
• Yue, F., Li, J., & Sun, J. (2020). Insights of Feature Fusion for Video Memorability Prediction. International Forum on Digital TV and Wireless Multimedia Communications, 239–248.
• Zhang, M., Zhong, W., Ye, L., Fang, L., & Zhang, Q. (2020). Synchronous Prediction of Continuous Affective Video Content Based on Multi-task Learning. International Forum on Digital TV and Wireless Multimedia Communications, 227–238.
• Martin, P.-E. (2020). Fine-grained action detection and classification from videos with spatio-temporal convolutional neural networks: Application to Table Tennis. [PhD thesis]. Université de Bordeaux.
• Lopez-Fuentes, L., Farasin, A., Zaffaroni, M., Skinnemoen, H., & Garza, P. (2020). Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data. Applied Sciences, 10(24), 8783.
• Jain, P., Schoen-Phelan, B., & Ross, R. (2020). Tri-Band Assessment of Multi-Spectral Satellite Data for Flood Detection. MACLEAN@ PKDD/ECML.
• Duong, D. Q., Le, Q. M., Nguyen-Tai, T.-L., Bo, D., Nguyen, D., Dao, M.-S., & Nguyen, B. T. (2020). Multi-source Machine Learning for AQI Estimation. 2020 IEEE International Conference on Big Data (Big Data), 4567–4576.
• Xue, J., Wang, Y., Xu, S., Shi, L., Wei, L., & Song, H. (2020). MVFNN: Multi-Vision Fusion Neural Network for Fake News Picture Detection. International Conference on Computer Animation and Social Agents, 112–119.
• Weber, E., Marzo, N., Papadopoulos, D. P., Biswas, A., Lapedriza, A., Ofli, F., Imran, M., & Torralba, A. (2020). Detecting natural disasters, damage, and incidents in the wild. European Conference on Computer Vision, 331–350.
• Duong, N. (2020). Contributions in Audio Modeling and Multimodal Data Analysis via Machine Learning [PhD thesis]. Université de Rennes 1.
• Thao, H. T. P. (2020). Deep Neural Networks for Predicting Affective Responses from Movies. Proceedings of the 28th ACM International Conference on Multimedia, 4743–4747.
• Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Adrian Krenzer, F. P. (2020). Bigger Networks are not Always Better: Deep Convolutional Neural Networks for Automated Polyp Segmentation. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Ahmed, A. M. A. (2020). Generative Adversarial Networks for Automatic Polyp Segmentation. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Khadka, R. (2020). Transfer of Knowledge: Fine-tuning for Polyp Segmentation with Attention. Working Notes Proceedings of the MediaEval 2020 Workshop.
• HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Network and U-Net for Polyps Segmentation. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• A Temporal-Spatial Attention Model for Medical Image Detection. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Debapriya Banik, D. B. (2020). Deep Conditional Adversarial Learning for Polyp Segmentation. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Sahadev Poudel, S.-W. L. (2020). Automatic Polyp Segmentation Using Channel-Spatial Attention with Deep Supervision. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Tomar, N. K. (2020). Automatic Polyp Segmentation Using Fully Convolutional Neural Network. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Automatic Polyp Segmentation via Parallel Reverse Attention Network. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• George Batchkala, S. A. (2020). Real-Time Polyp Segmentation Using U-Net with IoU Loss. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Depth-Wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Intestinal Tract. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ for Polyps Segmentation. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Shruti Shrestha, S. A., Bishesh Khanal. (2020). Ensemble U-Net Model for Efficient Polyp Segmentation. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Sabari Nathan, S. R. (2020). Efficient Supervision Net: Polyp Segmentation Using EfficientNet and Attention Unit. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Jaeyong Kang, J. G. (2020). KD-ResUNet++: Automatic Polyp Segmentation via Self-Knowledge Distillation. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Automatic Polyp Segmentation Using U-Net-ResNet50. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Manfred Moosleitner, G. S., Benjamin Murauer. (2020). Detecting Conspiracy Tweets Using Support Vector Machines. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Chahat Raj, M. M. (2020). MediaEval 2020: An Ensemble-based Multimodal Approach for Coronavirus and 5G Conspiracy Tweet Detection. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Andrey Malakhov, S. B., Alessandro Patruno. (2020). Fake News Classification with BERT. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Manh Duc Tuan Nguyen, M. Q. N. P. (2020). FakeNews Detection Using Pre-trained Language Models and Graph Convolutional Networks. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Fake News Detection in Social Media Using Graph Neural Networks and NLP Techniques: A COVID-19 Use-Case. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Gullal Singh Cheema, R. E., Sherzod Hakimov. (2020). TIB’s Visual Analytics Group at MediaEval ’20: Detecting Fake News on Corona Virus and 5G Conspiracy. Working Notes Proceedings of the MediaEval 2020 Workshop.
• de Rijk, L. (2020). You Said It? How Mis- and Disinformation Tweets Surrounding the Corona-5G-Conspiracy Communicate Through Implying. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Ferdinand Schaal, J. P. (2020). Using a Word Analysis Method and GNNs to Classify Misinformation Related to 5G-Conspiracy and the COVID-19 Pandemic. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Claveau, V. (2020). Detecting Fake News in Tweets from Text and Propagation Graph: IRISA’s Paritcipation to the FakeNews Task at MediaEval 2020. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Daniel Thilo Schroeder, J. L., Konstantin Pogorelov. (2020). Evaluating Standard Classifiers for Detecting COVID-19 Related Misinformation. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Enriching Content Analysis of Tweets Using Community Discovery Graph Analysis. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Detecting Conspiracy Theories from Tweets: Textual and Structural Approaches. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Pehlivan, Z. (2020). On the pursuit of Fake News : From Graph Convolutional Networks to Time Series. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Olga Papadopoulou, S. P., Giorgos Kordopatis-Zilos. (2020). MeVer Team Tackling Corona Virus and 5G Conspiracy Using Ensemble Classification Based on BERT. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable? (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Dazhan Xu, G. S., Xiaoyu Wu. (2020). Media Memorability Prediction Based on Machine Learning. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Investigating Memorability of Dynamic Media. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Lorin Sweeney, A. F. S., Graham Healy. (2020). Leveraging Audio Gestalt to Predict Media Memorability. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Essex-NLIP at MediaEval Predicting Media Memorability 2020 Task. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Multi-Modal Ensemble Models for Predicting Video Memorability. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Predicting Media Memorability with Audio, Video, and Text representations. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Predicting Media Memorability from a Multimodal Late Fusion of Self-Attention and LSTM Models. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• MediaEval 2020: Emotion and Theme Recognition in Music Using Jamendo. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Recognizing Song Mood and Theme: Leveraging Ensembles of Tag Groups. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Srividya Tirunellai Rajamani, B. S., Kumar Rajamani. (2020). Emotion and Theme Recognition in Music Using Attention-Based Methods. Working Notes Proceedings of the MediaEval 2020 Workshop.
• HCMUS at MediaEval 2020: Emotion Classification Using Wavenet Feature with SpecAugment and EfficientNet. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Emotion and Themes Recognition in Music with Convolutional and Recurrent Attention-Blocks. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• MediaEval 2020 Emotion and Theme Recognition in Music Task: Loss Function Approaches for Multi-label Music Tagging. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Alish Dipani, V. B., Gaurav Iyer. (2020). Recognizing Music Mood and Theme Using Convolutional Neural Networks and Attention. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Sports Video Classification: Classification of Strokes in Table Tennis for MediaEval 2020. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Soichiro Sato, M. A. (2020). Leveraging Human Pose Estimation Model for Stroke Classification in Table Tennis. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Jordan Calandre, L. M., Renaud Péteri. (2020). Four-Stream Network and Dynamic Images for Sports Video Classification: Classification of Strokes in Table Tennis. Working Notes Proceedings of the MediaEval 2020 Workshop.
• HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table Tennis Strokes Classification Task. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Spatio-Temporal Based Table Tennis Hit Assessment Using LSTM Algorithm. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• The Flood-Related Multimedia Task at MediaEval 2020. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Rabiul Islam Jony, A. W. (2020). Flood Detection in Twitter Using a Novel Learning Method for Neural Networks. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Theodore Nikoletopoulos, C. W. (2020). A Tweet Text Binary Artificial Neural Network Classifier. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Floods Detection in Twitter Text and Images. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Flood Detection via Twitter Streams Using Textual and Visual Features. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Ensemble Based Method for the Classification of Flooding Event Using Social Media Data. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Dat Q. Duong, D. T. N., Quang M. Le. (2020). A2QI: An Approach for Air Pollution Estimation in MediaEval 2020. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Trung-Quan Nguyen, L. T. T. N., Dang-Hieu Nguyen. (2020). Personal Air Quality Index Prediction Using Inverse Distance Weighting Method. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Trung-Quan Nguyen, L. T. T. N., Dang-Hieu Nguyen. (2020). Use Visual Features From Surrounding Scenes to Improve Personal Air Quality Data Prediction Performance. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Insights for Wellbeing: Predicting Personal Air Quality Index Using Regression Approach. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Pixel Privacy: Quality Camouflage for Social Images. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Zhao, Z. (2020). Fooling Blind Image Quality Assessment by Optimizing a Human-Understandable Color Filter. Working Notes Proceedings of the MediaEval 2020 Workshop.
• MediaEval 2020: Maintaining Human-Imperceptibility of Image Adversarial Attack by Using Human-Aware Sensitivity Map. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• HCMUS at Pixel Privacy 2020: Quality Camouflage with Back Propagation and Image Enhancement. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Benoit Bonnet, P. B., Teddy Furon. (2020). Fooling an Automatic Image Quality Estimator. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Laura Cabrera-Quiros, H. H., Jose Vargas-Quirós. (2020). No-Audio Multimodal Speech Detection Task at MediaEval 2020. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Xinsheng Wang, O. S., Jihua Zhu. (2020). Multimodal Fusion of Body Movement Signals for No-audio Speech Detection. Working Notes Proceedings of the MediaEval 2020 Workshop.
• Benjamin Kille, Ö. Ö., Andreas Lommatzsch. (2020). News Images in MediaEval 2020. Working Notes Proceedings of the MediaEval 2020 Workshop.
• HCMUS at MediaEval 2020: Image-Text Fusion for Automatic News-Images Re-Matching. (2020). Working Notes Proceedings of the MediaEval 2020 Workshop.
• Zhang, T., Wang, D., Chen, H., Zeng, Z., Guo, W., Miao, C., & Cui, L. (2020). BDANN: BERT-based domain adaptation neural network for multi-modal fake news detection. 2020 International Joint Conference on Neural Networks (IJCNN), 1–8.
• Giachanou, A., Zhang, G., & Rosso, P. (2020). Multimodal fake news detection with textual, visual and semantic information. International Conference on Text, Speech, and Dialogue, 30–38.
• Tanwar, V., & Sharma, K. (2020). Multi-Model Fake News Detection based on Concatenation of Visual Latent Features. 2020 International Conference on Communication and Signal Processing (ICCSP), 1344–1348.
• Ou, Y., Chen, Z., & Wu, F. (2020). Multimodal local-global attention network for affective video content analysis. IEEE Transactions on Circuits and Systems for Video Technology, 31(5), 1901–1914.
• Rambour, C., Audebert, N., Koeniguer, E., Le Saux, B., Crucianu, M., & Datcu, M. (2020). Flood detection in time series of optical and sar images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 1343–1346.
• Bouhlel, N., Feki, G., & Amar, C. B. (2020). Hypergraph-based image search reranking with elastic net regularized regression. Multimedia Tools and Applications, 79(41), 30257–30280.
• Astefanoaei, M., & Collignon, N. (2020). Hyperbolic Embeddings for Music Taxonomy. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), 38–42.
• Armitage, J., Kacupaj, E., Tahmasebzadeh, G., Maleshkova, M., Ewerth, R., & Lehmann, J. (2020). Mlm: A benchmark dataset for multitask learning with multiple languages and modalities. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2967–2974.
• Rana, D. P., Bawkar, S., Jain, M., Bothra, S., & Baldaniya, S. (2020). Image Based Fake Tweet Retrieval (IBFTR). 2020 International Conference for Emerging Technology (INCET), 1–8.
• Koutini, K., Eghbal-Zadeh, H., Haunschmid, V., Primus, P., Chowdhury, S., & Widmer, G. (2020). Receptive-field regularized CNNs for music classification and tagging. ArXiv Preprint ArXiv:2007.13503.
• Gao, Z., Qiu, L., Qi, P., & Sun, Y. (2020). A Novel Music Emotion Recognition Model for Scratch-generated Music. 2020 International Wireless Communications and Mobile Computing (IWCMC), 1794–1799.
• Patel, S. (2020). Precise Image Exploration With Cluster Analysis [PhD thesis].
• Deekshitha, G., & Mary, L. (2020). Multilingual spoken term detection: a review. International Journal of Speech Technology, 23(3), 653–667.
• Fadel, S. G., & Torres, R. da S. (2020). Neural relational inference for disaster multimedia retrieval. Multimedia Tools and Applications, 79(35), 26735–26746.
• Tahmasebzadeh, G., Hakimov, S., Müller-Budack, E., & Ewerth, R. (2020). A Feature Analysis for Multimodal News Retrieval. ArXiv Preprint ArXiv:2007.06390.
• Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2020). Two-stream deep feature modelling for automated video endoscopy data analysis. International Conference on Medical Image Computing and Computer-Assisted Intervention, 742–751.
• Pereira, J., Monteiro, J., Silva, J., Estima, J., & Martins, B. (2020). Assessing flood severity from crowdsourced social media photos with deep neural networks. Multimedia Tools and Applications, 79(35), 26197–26223.
• Osailan, S. (2020). Topic Modeling Location-Based Social Media Applications [PhD thesis]. The Claremont Graduate University.
• Feng, Y., Brenner, C., & Sester, M. (2020). Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: A case study of Hurricane Harvey. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 301–319.
• Zhu, Y., Chen, Z., & Wu, F. (2020). Affective Video Content Analysis via Multimodal Deep Quality Embedding Network. IEEE Transactions on Affective Computing.
• Gu, C., Wu, X., & Wang, S. (2020). Violent Video Detection Based on Semantic Correspondence. IEEE Access, 8, 85958–85967.
• Maseri, A. N., Norman, A. A., Eke, C. I., Ahmad, A., & Molok, N. N. A. (2020). Socio-Technical Mitigation Effort to Combat Cyber Propaganda: A Systematic Literature Mapping. IEEE Access, 8, 92929–92944.
• Quan, K.-A. C., Nguyen, V.-T., Nguyen, T.-C., Nguyen, T. V., & Tran, M.-T. (2020). Flood level prediction via human pose estimation from social media images. Proceedings of the 2020 International Conference on Multimedia Retrieval, 479–485.
• Khan, O. S. (2020). An Interactive Learning System for Large-Scale Multimedia Analytics. Proceedings of the 2020 International Conference on Multimedia Retrieval, 368–372.
• Tahmasebzadeh, G., Hakimov, S., Müller-Budack, E., & Ewerth, R. (2020). A Feature Analysis for Multimodal News Retrieval. ArXiv Preprint ArXiv:2007.06390.
• Sanchez-Matilla, R., Li, C. Y., Shamsabadi, A. S., Mazzon, R., & Cavallaro, A. (2020). Exploiting vulnerabilities of deep neural networks for privacy protection. IEEE Transactions on Multimedia, 22(7), 1862–1873.
• Schwarz, S., Theóphilo, A., & Rocha, A. (2020). EMET: Embeddings from Multilingual-Encoder Transformer for Fake News Detection. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2777–2781.
• Peixoto, B., Lavi, B., Bestagini, P., Dias, Z., & Rocha, A. (2020). Multimodal Violence Detection in Videos. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2957–2961.
• Ştefan, L.-D., Constantin, M. G., & Ionescu, B. (2020). System Fusion with Deep Ensembles. Proceedings of the 2020 International Conference on Multimedia Retrieval, 256–260.
• Chandra, Y., Tag, B., Peiris, R. L., & Minamizawa, K. (2020). Preliminary Investigation of Across-Body Vibrotactile Pattern for the Design of Affective Furniture. 2020 IEEE Haptics Symposium (HAPTICS), 671–676.
• Shamsabadi, A. S., Oh, C., & Cavallaro, A. (2020). EdgeFool: An Adversarial Image Enhancement Filter. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1898–1902.
• Martin, P.-E., Benois-Pineau, J., Péteri, R., & Morlier, J. (2020). Fine grained sport action recognition with Twin spatio-temporal convolutional neural networks. Multimedia Tools and Applications, 79(27), 20429–20447.
• Russo, M., Kraljević, L., Stella, M., & Sikora, M. (2020). Cochleogram-based approach for detecting perceived emotions in music. Information Processing & Management, 57(5), 102270.
• Ionescu, B., Rohm, M., Boteanu, B., Gı̂nscą Alexandru Lucian, Lupu, M., & Mueller, H. (2020). Benchmarking Image Retrieval Diversification Techniques for Social Media. IEEE Transactions on Multimedia.
• Constantin, M. G., Stefan, L. D., Ionescu, B., Demarty, C.-H., Sjoberg, M., Schedl, M., & Gravier, G. (2020). Affect in multimedia: Benchmarking violent scenes detection. IEEE Transactions on Affective Computing.
• Du, P., Li, X., & Gao, Y. (2020). Dynamic Music emotion recognition based on CNN-BiLSTM. 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), 1372–1376.
• Bouhlel, N., Feki, G., & Amar, C. B. (2020). Visual Re-Ranking via Adaptive Collaborative Hypergraph Learning for Image Retrieval. European Conference on Information Retrieval, 511–526.
• Goynuk, B., & Altingovde, I. S. (2020). Supervised learning methods for diversification of image search results. European Conference on Information Retrieval, 158–165.
• Formal, T., Clinchant, S., Renders, J.-M., Lee, S., & Cho, G. H. (2020). Learning to Rank Images with Cross-Modal Graph Convolutions. European Conference on Information Retrieval, 589–604.
• Medina, Y. O., Beltrán, J. R., & Baldassarri, S. (2020). Emotional classification of music using neural networks with the MediaEval dataset. Personal and Ubiquitous Computing, 1–13.
• Qian, X., Wu, Y., Li, M., Ren, Y., Jiang, S., & Li, Z. (2020). Last: Location-appearance-semantic-temporal clustering based POI summarization. IEEE Transactions on Multimedia.
• Atto, A. M., Benoit, A., & Lambert, P. (2020). Timed-image based deep learning for action recognition in video sequences. Pattern Recognition, 104, 107353.
• Sorussa, K., Choksuriwong, A., & Karnjanadecha, M. (2020). Emotion Classification System for Digital Music with a Cascaded Technique. ECTI Transactions on Computer and Information Technology (ECTI-CIT), 14(1), 53–66.
• Zhou, H., Yin, H., Zheng, H., & Li, Y. (2020). A survey on multi-modal social event detection. Knowledge-Based Systems, 195, 105695.
• Cao, J., Qi, P., Sheng, Q., Yang, T., Guo, J., & Li, J. (2020). Exploring the role of visual content in fake news detection. Disinformation, Misinformation, and Fake News in Social Media, 141–161.
• Jain, P., Schoen-Phelan, B., & Ross, R. (2020). Automatic flood detection in SentineI-2 images using deep convolutional neural networks. Proceedings of the 35th Annual ACM Symposium on Applied Computing, 617–623.
• Schaible, J., Breuer, T., Tavakolpoursaleh, N., Müller, B., Wolff, B., & Schaer, P. (2020). Evaluation Infrastructures for Academic Shared Tasks. Datenbank-Spektrum, 1–8.
• Kang, S. K., Hwang, J., & Yu, H. (2020). Multi-modal component embedding for fake news detection. 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), 1–6.
• Xie, B., Kim, J. C., & Park, C. H. (2020). Musical emotion recognition with spectral feature extraction based on a sinusoidal model with model-based and deep-learning approaches. Applied Sciences, 10(3), 902.
• Cheuk, K. W., Luo, Y.-J., Balamurali, B. T., Roig, G., & Herremans, D. (2020). Regression-based music emotion prediction using triplet neural networks. 2020 International Joint Conference on Neural Networks (IJCNN), 1–7.
• Bouhlel, N., Feki, G., Ammar, A. B., & Amar, C. B. (2020). Hypergraph learning with collaborative representation for image search reranking. International Journal of Multimedia Information Retrieval, 1–10.
• Lopez-Otero, P., Parapar, J., & Barreiro, A. (2020). Statistical language models for query-by-example spoken document retrieval. Multimedia Tools and Applications, 79(11), 7927–7949.
• Guo, X., Zhong, W., Ye, L., Fang, L., Heng, Y., & Zhang, Q. (2020). Global Affective Video Content Regression Based on Complementary Audio-Visual Features. International Conference on Multimedia Modeling, 540–550.
• Borgli, H., Thambawita, V., Smedsrud, P. H., Hicks, S., Jha, D., Eskeland, S. L., Randel, K. R., Pogorelov, K., Lux, M., Nguyen, D. T. D., & others. (2020). HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Scientific Data, 7(1), 1–14.
• Shamsabadi, A. S., Oh, C., & Cavallaro, A. (2020). Edgefool: an Adversarial Image Enhancement Filter. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1898–1902.
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• Liu, Y., Gu, Z., & Ko, T. H. (2018). Analyzing Human Behavior in Subspace: Dimensionality Reduction+ Classification. MediaEval Working Notes Proceedings.
• Cabrera-Quiros, L., Gedik, E., & Hung, H. (2018). Transductive Parameter Transfer, Bags of Dense Trajectories and MILES for No-Audio Multimodal Speech Detection. MediaEval Working Notes Proceedings.
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• Ahmad, S., Ahmad, K., Ahmad, N., & Conci, N. (2017). Convolutional Neural Networks for Disaster Images Retrieval. MediaEval Working Notes Proceedings.
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• Sutcliffe, R. F. E., & Maidín, Ó. (2017). … C@merata task at MediaEval 2017: Natural Language Queries about Music, their JSON Representations, and Matching Passages in MusicXML Scores. MediaEval Working Notes Proceedings.
• Petscharnig, S., Schöffmann, K., & Lux, M. (2017). An Inception-like CNN Architecture for GI Disease and Anatomical Landmark Classification. MediaEval Working Notes Proceedings.
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• Gündogdu, B. (2017). Keyword search for low resource languages.
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