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 885 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.

  • 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.
  • Jha, D., Smedsrud, P. H., Riegler, M. A., Halvorsen, P., de Lange, T., Johansen, D., & Johansen, H. D. (2020). Kvasir-seg: A segmented polyp dataset. International Conference on Multimedia Modeling, 451–462.
  • Ram, D., Miculicich, L., & Bourlard, H. (2020). Neural network based end-to-end query by example spoken term detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 1416–1427.
  • Shamsabadi, A. S., Sanchez-Matilla, R., & Cavallaro, A. (2020). Colorfool: Semantic adversarial colorization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1151–1160.
  • Ali, A., Senan, N., Yanto, I. T. R., & Lashari, S. A. (2019). Classification Performance of Violence Content by Deep Neural Network with Monarch Butterfly Optimization. International Journal of Advanced Computer Science and Applications, 10(12).
  • May, L., & Casey, M. (2019). Familiar Feelings: Listener-Rated Familiarity in Music Emotion Recognition. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 446–453.
  • Yang, Z., Li, Q., Wenyin, L., & Lv, J. (2019). Shared Multi-view Data Representation for Multi-domain Event Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • Pereira, J., Monteiro, J., Estima, J., & Martins, B. (2019). Assessing flood severity from georeferenced photos. Proceedings of the 13th Workshop on Geographic Information Retrieval, 1–10.
  • GÖYNÜK, B. (2019). Supervised Learning for Image Search Result Diversification [PhD thesis]. Middle East Technical University.
  • Sandberg, M. A. (2019). Music and Sport: An Explorative Study using Unsupervised Machine Learning [Master's thesis].
  • mo Yang, Y. (2019). Short-time Emotion Tracker Using a Convolutional Autoencoder. ECE Department at University of Rochester.
  • Jony, R. I., Woodley, A., & Perrin, D. (2019). Flood Detection in Social Media Images using Visual Features and Metadata. 2019 Digital Image Computing: Techniques and Applications (DICTA), 1–8.
  • Liu, H., Fang, Y., & Huang, Q. (2019). Music Emotion Recognition Using a Variant of Recurrent Neural Network. 2018 International Conference on Mathematics, Modeling, Simulation and Statistics Application (MMSSA 2018).
  • Fukayama, S., & Goto, M. (2019). System, method, and computer program for estimation of target value. In US Patent App. 16/070,144.
  • Guo, J., Song, B., Zhang, P., Ma, M., & Luo, W. (2019). Affective video content analysis based on multimodal data fusion in heterogeneous networks. Information Fusion, 51, 224–232.
  • Muszynski, M., Tian, L., Lai, C., Moore, J., Kostoulas, T., Lombardo, P., Pun, T., & Chanel, G. (2019). Recognizing Induced Emotions of Movie Audiences From Multimodal Information. IEEE Transactions on Affective Computing.
  • Gialampoukidis, I., Chatzilari, E., Nikolopoulos, S., Vrochidis, S., & Kompatsiaris, I. (2019). Multimodal Fusion of Big Multimedia Data. Big Data Analytics for Large-Scale Multimedia Search, 121.
  • Larson, M., Choi, J., Slokom, M., Erkin, Z., Friedland, G., & de Vries, A. P. (2019). Privacy and Audiovisual Content: Protecting Users as Big Multimedia Data Grows Bigger. Big Data Analytics for Large-Scale Multimedia Search, 183.
  • Bischke, B., Borth, D., & Dengel, A. (2019). Large-Scale Social Multimedia Analysis. Big Data Analytics for Large-Scale Multimedia Search, 157.
  • Khattar, D. (2019). Neural Approaches Towards Computational Journalism [PhD thesis]. In PhD Thesis. International Institute of Information Technology Hyderabad.
  • Madhavi, M. C., & Patil, H. A. (2019). Vocal Tract Length Normalization using a Gaussian mixture model framework for query-by-example spoken term detection. Computer Speech & Language, 58, 175–202.
  • Moumtzidou, A., Bakratsas, M., Andreadis, S., Gialampoukidis, I., Vrochidis, S., & Kompatsiaris, I. (2019). Road passability estimation using deep neural networks and sattelite image patches.
  • Li, C. Y., Shamsabadi, A. S., Sanchez-Matilla, R., Mazzon, R., & Cavallaro, A. (2019). Scene privacy protection. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2502–2506.
  • Engilberge, M., Chevallier, L., Pérez, P., & Cord, M. (2019). SoDeep: a Sorting Deep net to learn ranking loss surrogates. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 10792–10801.
  • Ibrahim, Z. A. A., Haidar, S., & Sbeity, I. (2019). Large-scale Text-based Video Classification using Contextual Features. European Journal of Electrical Engineering and Computer Science, 3(2).
  • Lago, F., Phan, Q. T., & Boato, G. (2019). Visual and Textual Analysis for Image Trustworthiness Assessment within Online News. Security and Communication Networks, 2019.
  • Peixoto, B., Lavi, B., Martin, J. P. P., Avila, S., Dias, Z., & Rocha, A. (2019). Toward Subjective Violence Detection in Videos. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 8276–8280.
  • Wang, S., Wang, C., Chen, T., Wang, Y., Shu, Y., & Ji, Q. (2019). Video Affective Content Analysis by Exploring Domain Knowledge. IEEE Transactions on Affective Computing.
  • Khattar, D., Goud, J. S., Gupta, M., & Varma, V. (2019). MVAE: Multimodal Variational Autoencoder for Fake News Detection. The World Wide Web Conference, 2915–2921.
  • Yuan, B., & Gao, X. (2019). Diversified textual features based image retrieval. Neurocomputing, 357, 116–124.
  • Benavent, X., Castellanos, A., de Ves, E., Garcı́a-Serrano Ana, & Cigarrán, J. (2019). FCA-based knowledge representation and local generalized linear models to address relevance and diversity in diverse social images. Future Generation Computer Systems, 100, 250–265.
  • Lux, M., Halvorsen, P., Dang-Nguyen, D.-T., Stensland, H., Kesavulu, M., Potthast, M., & Riegler, M. (2019). Summarizing E-Sports Matches and Tournaments. Workshop on Immersive Mixed and Virtual Environment Systems, Amherst, MA, USA.
  • Dong, Y., Yang, X., Zhao, X., & Li, J. (2019). Bidirectional Convolutional Recurrent Sparse Network (BCRSN): An Efficient Model for Music Emotion Recognition. IEEE Transactions on Multimedia.
  • Potthast, M., Gollub, T., Wiegmann, M., & Stein, B. (2019). TIRA Integrated Research Architecture. Information Retrieval Evaluation in a Changing World-Lessons Learned From, 20.
  • Dourado, I. C., Tabbone, S., & da Silva Torres, R. (2019). Event Prediction Based on Unsupervised Graph-Based Rank-Fusion Models. International Workshop on Graph-Based Representations in Pattern Recognition, 88–98.
  • Orjesek, R., Jarina, R., Chmulik, M., & Kuba, M. (2019). DNN Based Music Emotion Recognition from Raw Audio Signal. 2019 29th International Conference Radioelektronika (RADIOELEKTRONIKA), 1–4.
  • Wu, Z., Zhou, K., Liu, Y., Zhang, M., & Ma, S. (2019). Does Diversity Affect User Satisfaction in Image Search. ACM Transactions on Information Systems (TOIS), 37(3), 35.
  • Saravi, S., Kalawsky, R., Joannou, D., Rivas-Casado, M., Fu, G., & Meng, F. (2019). Use of artificial intelligence to improve resilience and preparedness against adverse flood events. Water, 11(5), 973.
  • Dourado, I. C., Tabbone, S., & da Silva Torres, R. (2019). Event Prediction Based on Unsupervised Graph-Based Rank-Fusion Models. International Workshop on Graph-Based Representations in Pattern Recognition, 88–98.
  • Lux, M., Halvorsen, P., Dang-Nguyen, D.-T., Stensland, H., Kesavulu, M., Potthast, M., & Riegler, M. (2019). Summarizing E-sports matches and tournaments: the example of counter-strike: global offensive. Proceedings of the 11th ACM Workshop on Immersive Mixed and Virtual Environment Systems, 13–18.
  • Ammar, S. M., Anjum, M., Rounak, T., Islam, M., Islam, T., & others. (2019). Using deep learning algorithms to detect violent activities [PhD thesis]. In PhD Thesis. BRAC University.
  • Ram, D., Miculicich, L., & Bourlard, H. (2019). Multilingual Bottleneck Features for Query by Example Spoken Term Detection. ArXiv Preprint ArXiv:1907.00443.
  • Abebe, M. A., Tekli, J., Getahun, F., Chbeir, R., & Tekli, G. (2019). Generic metadata representation framework for social-based event detection, description, and linkage. Knowledge-Based Systems.
  • Cogan, T., Cogan, M., & Tamil, L. (2019). MAPGI: Accurate identification of anatomical landmarks and diseased tissue in gastrointestinal tract using deep learning. Computers in Biology and Medicine, 111, 103351.
  • Tejedor, J., Toledano, D. T., Lopez-Otero, P., Docio-Fernandez, L., Peñagarikano, M., Rodriguez-Fuentes, L. J., & Moreno-Sandoval, A. (2019). Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation. EURASIP Journal on Audio, Speech, and Music Processing, 2019(1), 13.
  • Li, B., Chen, Z., Li, S., & Zheng, W.-S. (2019). Affective Video Content Analyses by Using Cross-Modal Embedding Learning Features. 2019 IEEE International Conference on Multimedia and Expo (ICME), 844–849.
  • Kirkerød, M. (2019). Unsupervised preprocessing of medical imaging data with generative adversarial networks [PhD thesis]. In Master Thesis.
  • Lommatzsch, A., Kille, B., Styp-Rekowski, K., Karl, M., & Pommering, J. (2019). A Framework for Analyzing News Images and Building Multimedia-Based Recommender. International Conference on Innovations for Community Services, 184–201.
  • Epure, E. V., Khlif, A., & Hennequin, R. (2019). Leveraging Knowledge Bases And Parallel Annotations For Music Genre Translation. ArXiv Preprint ArXiv:1907.08698.
  • Vishwakarma, D. K., Varshney, D., & Yadav, A. (2019). Detection and veracity analysis of fake news via scrapping and authenticating the web search. Cognitive Systems Research, 58, 217–229.
  • Chmulik, M., Jarina, R., Kuba, M., & Lieskovska, E. (2019). Continuous Music Emotion Recognition Using Selected Audio Features. 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), 589–592.
  • Gamage, C., Wijesinghe, I., Chitraranjan, C., & Perera, I. (2019). GI-Net: anomalies classification in gastrointestinal tract through endoscopic imagery with deep learning. 2019 Moratuwa Engineering Research Conference (MERCon), 66–71.
  • Wang, S., Hao, L., & Ji, Q. (2019). Knowledge-Augmented Multimodal Deep Regression Bayesian Networks for Emotion Video Tagging. IEEE Transactions on Multimedia, 22(4), 1084–1097.
  • Zhang, J., Zhao, Y., Cai, L., Tu, C., & Wei, W. (2019). Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context. ArXiv Preprint ArXiv:1909.01763.
  • Le, D. H. N. (2019). Multimodal person recognition in audio-visual streams. EPFL.
  • Singhal, S., Shah, R. R., Chakraborty, T., Kumaraguru, P., & Satoh, S. (2019). SpotFake: A Multi-modal Framework for Fake News Detection. 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), 39–47.
  • Yusuf, B., Gök, A., Gündogdu, B., Kose, O. D., & Saraclar, M. (2019). Temporally-Aware Acoustic Unit Discovery for Zerospeech 2019 Challenge. INTERSPEECH, 1098–1102.
  • Sarker, C., Mejias, L., Maire, F., & Woodley, A. (2019). Evaluation of the impact of image spatial resolution in designing a context-based fully convolution neural networks for flood mapping. 2019 Digital Image Computing: Techniques and Applications (DICTA), 1–8.
  • Quirós J Garcı́a, Baldassarri, S., Beltrán, J. R., Guiu, A., & Álvarez, P. (2019). An Automatic Emotion Recognition System for Annotating Spotify’s Songs. OTM Confederated International Conferences" On the Move to Meaningful Internet Systems", 345–362.
  • Sun, Q., Wang, L., Li, M., Zhang, L., & Yang, Y. (2019). A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning. ArXiv Preprint ArXiv:1910.05996.
  • Zhu, Y., Chen, Z., & Wu, F. (2019). Multimodal deep denoise framework for affective video content analysis. Proceedings of the 27th ACM International Conference on Multimedia, 130–138.
  • Fonseca, G. B., Patrocı́nio Jr Zenilton KG, Gravier, G., & Guimarães, S. J. F. (2019). Multimodal person discovery using label propagation over speaking faces graphs. Anais Estendidos Da XXXII Conference on Graphics, Patterns and Images, 126–132.
  • Hoang, T.-H., Nguyen, H.-D., Nguyen, V.-A., Nguyen, T.-A., Nguyen, V.-T., & Tran, M.-T. (2019). Enhancing Endoscopic Image Classification with Symptom Localization and Data Augmentation. Proceedings of the 27th ACM International Conference on Multimedia, 2578–2582.
  • Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., De Lange, T., Halvorsen, P., & Johansen, H. D. (2019). Resunet++: An advanced architecture for medical image segmentation. 2019 IEEE International Symposium on Multimedia (ISM), 225–2255.
  • Aslan, F., & Ekenel, H. K. (2019). Emotion Prediction in Movies Using Visual Features & Genre Information. 2019 4th International Conference on Computer Science and Engineering (UBMK), 1–5.
  • Lux, M., Riegler, M., Halvorsen, P., Dang-Nguyen, D.-T., & Potthast, M. (2019). Challenges for Multimedia Research in E-Sports Using Counter-Strike. In Savegame (pp. 197–206). Springer.
  • Pereira, J., Monteiro, J., Estima, J., & Martins, B. (2019). Assessing flood severity from georeferenced photos. Proceedings of the 13th Workshop on Geographic Information Retrieval, 1–10.
  • Yi, Y., Wang, H., & Li, Q. (2019). Affective video content analysis with adaptive fusion recurrent network. IEEE Transactions on Multimedia, 22(9), 2454–2466.
  • Bischke, B., Helber, P., Brugman, S., Basar, E., Zhao, Z., Larson, M., & Pogorelov, K. (2019). The Multimedia Satellite Task at MediaEval 2019. MediaEval Working Notes Proceedings.
  • Murtaza, M., Hanif, M., Tahir, M. A., & Rafi, M. (2019). Ensemble and Inference based Methods for Flood Severity Estimation Using Visual Data. MediaEval Working Notes Proceedings.
  • Quan-Chi, K.-A., Nguyen, T.-C., Nguyen, V.-T., & Tran, M.-T. (2019). Flood Event Analysis Base on Pose Estimation and Water-Related Scene Recognition. MediaEval Working Notes Proceedings.
  • Andreadis, S., Bakratsas, M., Giannakeris, P., Moumtzidou, A., Gialampoukidis, I., Vrochidis, S., & Kompatsiaris, I. (2019). Multimedia Analysis Techniques for Flood Detection Using Images, Articles and Satellite Imagery. MediaEval Working Notes Proceedings.
  • Zaffaroni, M., Fuentes, L. L., Farasin, A., Garza, P., & Skinnemoen, H. (2019). AI-Based Flood Event Understanding and Quantifying Using Online Media and Satellite Data. MediaEval Working Notes Proceedings.
  • Bruneau, P., & Tamisier, T. (2019). Transfer Learning and Mixed Input Deep Neural Networks for Estimating Flood Severity in News Content. MediaEval Working Notes Proceedings.
  • Ganapathy, H., Bandlamudi, G., L, Y., J, B., & Mirnalinee, T. T. (2019). Deep Learning Models for Estimation of Flood Severity Using Multimodal and Satellite Images. MediaEval Working Notes Proceedings.
  • Ahmad, K., Pogorelov, K., Ullah, M., Riegler, M., Conci, N., Langguth, J., & Al-Fuqaha, A. (2019). Multi-Modal Machine Learning for Floods Detection in News, Social Media and Satellite Sequences. MediaEval Working Notes Proceedings.
  • Feng, Y., Tang, S., Cheng, H., & Sester, M. (2019). Flood Level Estimation from News Articles and Flood Detection from Satellite Image Sequences. MediaEval Working Notes Proceedings.
  • Bînă, D., Vlad, G.-A., Onose, C., & Cercel, D.-C. (2019). Flood Severity Estimation in News Articles Using Deep Learning Approaches. MediaEval Working Notes Proceedings.
  • Strebl, J., Slijepcevic, D., Kirchknopf, A., Sakeena, M., Seidl, M., & Zeppelzauer, M. (2019). Estimation of Flood Level from Social Media Images. MediaEval Working Notes Proceedings.
  • Jain, P., Schoen-Phelan, B., & Ross, R. (2019). MediaEval2019: Flood Detection in Time Sequence Satellite Images. MediaEval Working Notes Proceedings.
  • Bischke, B., Brugman, S., & Helber, P. (2019). Flood Severity Estimation from Online News Images and Multi-Temporal Satellite Images Using Deep Neural Networks. MediaEval Working Notes Proceedings.
  • Constantin, M. G., Ionescu, B., Demarty, C.-H., Duong, N. Q. K., Alameda-Pineda, X., & Sjöberg, M. (2019). The Predicting Media Memorability Task at MediaEval 2019. MediaEval Working Notes Proceedings.
  • Azcona, D., Moreu, E., Hu, F., Ward, T. E., & Smeaton, A. F. (2019). Predicting Media Memorability Using Ensemble Models. MediaEval Working Notes Proceedings.
  • Tran, L.-V., Huynh, V.-L., & Tran, M.-T. (2019). Predicting Media Memorability Using Deep Features with Attention and Recurrent Network. MediaEval Working Notes Proceedings.
  • Reboud, A., Harrando, I., Laaksonen, J., Francis, D., Troncy, R., & Mantecón, H. L. (2019). Combining Textual and Visual Modeling for Predicting Media Memorability. MediaEval Working Notes Proceedings.
  • dos Santos, S. F., & Almeida, J. (2019). GIBIS at MediaEval 2019: Predicting Media Memorability Task. MediaEval Working Notes Proceedings.
  • Viola, A., & Yoon, S. (2019). A Hybrid Approach for Video Memorability Prediction. MediaEval Working Notes Proceedings.
  • Wang, S., Yao, L., Chen, J., & Jin, Q. (2019). RUC at MediaEval 2019: Video Memorability Prediction Based on Visual Textual and Concept Related Features. MediaEval Working Notes Proceedings.
  • Leyva, R., Doctor, F., de Herrera, A. G. S., & Sahab, S. (2019). Multimodal Deep Features Fusion for Video Memorability Prediction. MediaEval Working Notes Proceedings.
  • Constantin, M. G., Kang, C., Dinu, G., Dufaux, F., Valenzise, G., & Ionescu, B. (2019). Using Aesthetics and Action Recognition-Based Networks for the Prediction of Media Memorability. MediaEval Working Notes Proceedings.
  • Yi, S., Wang, X., & Yamasaki, T. (2019). Emotion and Theme Recognition of Music Using Convolutional Neural Networks. MediaEval Working Notes Proceedings.
  • Amiriparian, S., Gerczuk, M., Coutinho, E., Baird, A., Ottl, S., Milling, M., & Schuller, B. (2019). Emotion and Themes Recognition in Music Utilising Convolutional and Recurrent Neural Networks. MediaEval Working Notes Proceedings.
  • Hung, H.-T., Chen, Y.-H., Mayerl, M., Vötter, M., Zangerle, E., & Yang, Y.-H. (2019). MediaEval 2019 Emotion and Theme Recognition task: A VQ-VAE Based Approach. MediaEval Working Notes Proceedings.
  • Bogdanov, D., Porter, A., Tovstogan, P., & Won, M. (2019). MediaEval 2019: Emotion and Theme Recognition in Music Using Jamendo. MediaEval Working Notes Proceedings.
  • Mayerl, M., Vötter, M., Hung, H.-T., Chen, B., Yang, Y.-H., & Zangerle, E. (2019). Recognizing Song Mood and Theme Using Convolutional Recurrent Neural Networks. MediaEval Working Notes Proceedings.
  • Koutini, K., Chowdhury, S., Haunschmid, V., Eghbal-Zadeh, H., & Widmer, G. (2019). Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs. MediaEval Working Notes Proceedings.
  • Sukhavasi, M., & Adapa, S. (2019). Music Theme Recognition Using CNN and Self-Attention. MediaEval Working Notes Proceedings.
  • Hicks, S., Halvorsen, P., Haugen, T. B., Andersen, J. M., Witczak, O., Pogorelov, K., Hammer, H. L., Dang-Nguyen, D.-T., Lux, M., & Riegler, M. (2019). Medico Multimedia Task at MediaEval 2019. MediaEval Working Notes Proceedings.
  • Hicks, S., Halvorsen, P., Haugen, T. B., Andersen, J. M., Witczak, O., Pogorelov, K., Hammer, H. L., Dang-Nguyen, D.-T., Lux, M., & Riegler, M. (2019). Predicting Sperm Motility and Morphology Using Deep Learning and Handcrafted Features. MediaEval Working Notes Proceedings.
  • Hicks, S., Haugen, T. B., Halvorsen, P., & Riegler, M. (2019). Using Deep Learning to Predict Motility and Morphology of Human Sperm. MediaEval Working Notes Proceedings.
  • Thambawita, V., Halvorsen, P., Hammer, H., Riegler, M., & Haugen, T. B. (2019). Stacked Dense Optical Flows and Dropout Layers to Predict Sperm Motility and Morphology. MediaEval Working Notes Proceedings.
  • Thambawita, V., Halvorsen, P., Hammer, H., Riegler, M., & Haugen, T. B. (2019). Extracting Temporal Features into a Spatial Domain Using Autoencoders for Sperm Video Analysis. MediaEval Working Notes Proceedings.
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