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 1135 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.

• Yadav, A., Gaba, S., Khan, H., Budhiraja, I., Singh, A., & Singh, K. K. (2023). ETMA: Efficient Transformer-Based Multilevel Attention Framework for Multimodal Fake News Detection. IEEE Transactions on Computational Social Systems.
• Chen, J., Jia, C., Zheng, H., Chen, R., & Fu, C. (2023). Is multi-modal necessarily better? Robustness evaluation of multi-modal fake news detection. IEEE Transactions on Network Science and Engineering.
• Ayoughi, M., Mettes, P., & Groth, P. (2023). Self-Contained Entity Discovery from Captioned Videos. ACM Transactions on Multimedia Computing, Communications and Applications, 19(5s), 1–21.
• Zhang, M., Zhu, Y., Zhang, W., Zhu, Y., & Feng, T. (2023). Modularized composite attention network for continuous music emotion recognition. Multimedia Tools and Applications, 82(5), 7319–7341.
• Hosseini, M., Sabet, A. J., He, S., & Aguiar, D. (2023). Interpretable fake news detection with topic and deep variational models. Online Social Networks and Media, 36, 100249.
• Jing, J., Wu, H., Sun, J., Fang, X., & Zhang, H. (2023). Multimodal fake news detection via progressive fusion networks. Information Processing & Management, 60(1), 103120.
• Avramidis, K., Stewart, S., & Narayanan, S. (2023). On the Role of Visual Context in Enriching Music Representations. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5.
• Chadebecq, F., Lovat, L. B., & Stoyanov, D. (2023). Artificial intelligence and automation in endoscopy and surgery. Nature Reviews Gastroenterology & Hepatology, 20(3), 171–182.
• Singh, P., Srivastava, R., Rana, K. P. S., & Kumar, V. (2023). SEMI-FND: stacked ensemble based multimodal inferencing framework for faster fake news detection. Expert Systems with Applications, 215, 119302.
• Moriya, Y., & Jones, G. J. F. (2023). Improving Noise Robustness for Spoken Content Retrieval Using Semi-Supervised ASR and N-Best Transcripts for BERT-Based Ranking Models. 2022 IEEE Spoken Language Technology Workshop (SLT), 398–405.
• Takashima, N., Li, F., Grzegorzek, M., & Shirahama, K. (2023). Embedding-based Music Emotion Recognition Using Composite Loss. IEEE Access.
• Meel, P., & Vishwakarma, D. K. (2023). Multi-modal fusion using Fine-tuned Self-attention and transfer learning for veracity analysis of web information. Expert Systems with Applications, 229, 120537.
• Sharma, D. K., & Garg, S. (2023). IFND: a benchmark dataset for fake news detection. Complex & Intelligent Systems, 9(3), 2843–2863.
• Chen, Y., Li, D., Zhang, P., Sui, J., Lv, Q., Tun, L., & Shang, L. (2022). Cross-modal ambiguity learning for multimodal fake news detection. Proceedings of the ACM Web Conference 2022, 2897–2905.
• Zeng, Y., Wang, Y., Liao, D., Li, G., Huang, W., Xu, J., Cao, D., & Man, H. (2022). Keyword-based diverse image retrieval with variational multiple instance graph. IEEE Transactions on Neural Networks and Learning Systems.
• Singhal, S., Pandey, T., Mrig, S., Shah, R. R., & Kumaraguru, P. (2022). Leveraging intra and inter modality relationship for multimodal fake news detection. Companion Proceedings of the Web Conference 2022, 726–734.
• Fu, B., & Sui, J. (2022). Multi-modal affine fusion network for social media rumor detection. PeerJ Computer Science, 8, e928.
• Guo, H., Huang, T., Huang, H., Fan, M., & Friedland, G. (2022). Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF. ArXiv Preprint ArXiv:2205.00377.
• Figuerêdo, J. S. L., & Calumby, R. T. (2022). Unsupervised query-adaptive implicit subtopic discovery for diverse image retrieval based on intrinsic cluster quality. Multimedia Tools and Applications, 81(30), 42991–43011.
• Singh, P., Srivastava, R., Rana, K. P. S., & Kumar, V. (2022). SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection. ArXiv Preprint ArXiv:2205.08159.
• Khouakhi, A., Zawadzka, J., & Truckell, I. (2022). The need for training and benchmark datasets for convolutional neural networks in flood applications. Hydrology Research, 53(6), 795–806.
• Khouakhi, A., Zawadzka, J., & Truckell, I. (2022). The need for training and benchmark datasets for convolutional neural networks in flood applications. Hydrology Research, 53(6), 795–806.
• Alfarano, G. (2022). Detecting fake news using natural language processing [PhD thesis]. Politecnico di Torino.
• Song, X. (2022). Innovation and Practice of Music Education Path in Colleges and Universities Under the Popularization of 5g Network.
• Lu, Y., Yu, Z., Liu, J., An, Q., Chen, C., Li, Y., & Wang, Y. (2022). Assessing systemic vascular resistance using arteriolar pulse transit time based on multi-wavelength photoplethysmography. Physiological Measurement, 43(7), 075005.
• Lu, Y., Yu, Z., Liu, J., An, Q., Chen, C., Li, Y., & Wang, Y. (2022). Assessing systemic vascular resistance using arteriolar pulse transit time based on multi-wavelength photoplethysmography. Physiological Measurement, 43(7), 075005.
• Wei, P., Wu, F., Sun, Y., Zhou, H., & Jing, X.-Y. (2022). Modality and Event Adversarial Networks for Multi-Modal Fake News Detection. IEEE Signal Processing Letters, 29, 1382–1386.
• Ottl, S., Amiriparian, S., Gerczuk, M., & Schuller, B. W. (2022). motilitAI: A machine learning framework for automatic prediction of human sperm motility. Iscience, 25(8).
• Tsai, Y.-C., Pan, T.-Y., Kao, T.-Y., Yang, Y.-H., & Hu, M.-C. (2022). Emvgan: Emotion-aware music-video common representation learning via generative adversarial networks. Proceedings of the 2022 International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia, 13–18.
• Harrando, I. (2022). Representation, information extraction, and summarization for automatic multimedia understanding [PhD thesis]. Sorbonne Université.
• Ahmad, K., Ayub, M. A., Khan, J., Ahmad, N., & Al-Fuqaha, A. (2022). Social media as an instant source of feedback on water quality. IEEE Transactions on Technology and Society.
• Wu, C., Wu, F., Qi, T., Zhang, C., Huang, Y., & Xu, T. (2022). Mm-rec: Visiolinguistic model empowered multimodal news recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2560–2564.
• Patra, A. (2022). Deep learning for automated polyp detection and localization in colonoscopy [Master's thesis]. OsloMet-storbyuniversitetet.
• Constantin, M. G., & Ionescu, B. (2022). Two-Stage Spatio-Temporal Vision Transformer for the Detection of Violent Scenes. 2022 14th International Conference on Communications (COMM), 1–5.
• Tovstogan, P., & others. (2022). Exploration of music collections with audio embeddings [PhD thesis]. Universitat Pompeu Fabra.
• Storås, A. M., Strümke, I., Riegler, M. A., & Halvorsen, P. (2022). Explainability methods for machine learning systems for multimodal medical datasets: research proposal. Proceedings of the 13th ACM Multimedia Systems Conference, 347–351.
• Ştefan, L.-D., Constantin, M. G., Dogariu, M., & Ionescu, B. (2022). Overview of imagecleffusion 2022 task-ensembling methods for media interestingness prediction and result diversification. CLEF2022 Working Notes, CEUR Workshop Proceedings, CEUR-WS. Org, Bologna, Italy.
• Constantin, M. G., Ştefan, L.-D., Dogariu, M., & Ionescu, B. (2022). Ai multimedia lab at imagecleffusion 2022: Deepfusion methods for ensembling in diverse scenarios. CLEF2022 Working Notes, CEUR Workshop Proceedings, CEUR-WS. Org, Bologna, Italy.
• Jaisakthi, S. M., & Dhanya, P. R. (2022). Social Media Flood Image Classification Using Transfer Learning with EfficientNet Variants. In Communication and Intelligent Systems: Proceedings of ICCIS 2021 (pp. 759–770). Springer.
• Pu, Y., Wu, X., Wang, S., Huang, Y., Liu, Z., & Gu, C. (2022). Semantic multimodal violence detection based on local-to-global embedding. Neurocomputing, 514, 148–161.
• Xia, Y., Xu, F., & others. (2022). Study on Music Emotion Recognition Based on the Machine Learning Model Clustering Algorithm. Mathematical Problems in Engineering, 2022.
• Jacobsen, F. L. (2022). Estimating Predictive Uncertainty in Gastrointestinal Image Segmentation [Master's thesis].
• Wang, Q., Xiang, X., & Zhao, J. (2022). ML-TFN: Multi Layers Tensor Fusion Network for Affective Video Content Analysis. International Conference on Neural Computing for Advanced Applications, 184–196.
• Akbar, A., Tahir, M. A., & Rafi, M. (2022). Transboundary Haze Prediction: Towards Time Series Forecasting of Cross-Data Analytics for Haze Prediction. 2022 International Conference on Emerging Trends in Smart Technologies (ICETST), 1–6.
• Muszynski, M., Morgenroth, E., Vilaclara, L., Van De Ville, D., & Vuilleumier, P. (2022). Impact of aesthetic movie highlights on semantics and emotions: a preliminary analysis. Companion Publication of the 2022 International Conference on Multimodal Interaction, 52–60.
• Xiao, P., Gou, P., Wang, B., Deng, E., & Zhao, P. (2022). Fine-Grained Gastrointestinal Endoscopy Image Categorization. Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences, 239–242.
• Herrera Alba Garcı́a Deco, Constantin, M. G., Demarty, C.-H., Fosco, C., Halder, S., Healy, G., Ionescu, B., Matran-Fernandez, A., Smeaton, A. F., Sultana, M., & others. (2022). Experiences from the MediaEval Predicting Media Memorability Task. ArXiv Preprint ArXiv:2212.03955.
• Hu, L., Zhao, Z., Ge, X., Song, X., & Nie, L. (2022). MMNet: Multi-modal Fusion with Mutual Learning Network for Fake News Detection. ArXiv Preprint ArXiv:2212.05699.
• Wang, J.-H., Norouzi, M., & Tsai, S. M. (2022). Multimodal Content Veracity Assessment with Bidirectional Transformers and Self-Attention-based Bi-GRU Networks. 2022 IEEE Eighth International Conference on Multimedia Big Data (BigMM), 133–137.
• Zhang, Y., Shao, Y., Zhang, X., Wan, W., Li, J., & Sun, J. (2022). BERT Based Fake News Detection Model. Training, 1530, 383.
• Varshney, D., & Vishwakarma, D. K. (2022). A unified approach of detecting misleading images via tracing its instances on web and analyzing its past context for the verification of multimedia content. International Journal of Multimedia Information Retrieval, 11(3), 445–459.
• Orjesek, R., Jarina, R., & Chmulik, M. (2022). End-to-end music emotion variation detection using iteratively reconstructed deep features. Multimedia Tools and Applications, 81(4), 5017–5031.
• Liu, R., & Wu, X. (2022). Multimodal Attention Network for Violence Detection. 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), 503–506. https://doi.org/10.1109/ICCECE54139.2022.9712676
• Singh, B., & Sharma, D. K. (2022). Predicting image credibility in fake news over social media using multi-modal approach. Neural Computing and Applications, 34(24), 21503–21517.
• Raj, C., & Meel, P. (2022). People lie, actions Don’t! Modeling infodemic proliferation predictors among social media users. Technology in Society, 68, 101930.
• Panagiotopoulos, A., Kordopatis-Zilos, G., & Papadopoulos, S. (2022). Leveraging Selective Prediction for Reliable Image Geolocation. International Conference on Multimedia Modeling, 369–381.
• Algiriyage, N., Prasanna, R., Stock, K., Doyle, E. E. H., & Johnston, D. (2022). Multi-source multimodal data and deep learning for disaster response: a systematic review. SN Computer Science, 3, 1–29.
• Manco, I., Benetos, E., Quinton, E., & Fazekas, G. (2022). Learning music audio representations via weak language supervision. ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 456–460.
• Constantin, M. G., Ştefan, L.-D., & Ionescu, B. (2022). Exploring deep fusion ensembling for automatic visual interestingness prediction. Human Perception of Visual Information: Psychological and Computational Perspectives, 33–58.
• Raj, C., & Meel, P. (2022). ARCNN framework for multimodal infodemic detection. Neural Networks, 146, 36–68.
• Xiao, K., Qian, Z., & Qin, B. (2022). A survey of data representation for multi-modality event detection and evolution. Applied Sciences, 12(4), 2204.
• Ali, H., Gilani, S. O., Khan, M. J., Waris, A., Khattak, M. K., & Jamil, M. (2022). Predicting Episodic Video Memorability Using Deep Features Fusion Strategy. 2022 IEEE/ACIS 20th International Conference on Software Engineering Research, Management and Applications (SERA), 39–46.
• Elsaeed, E., Ouda, O., Elmogy, M. M., Atwan, A., & El-Daydamony, E. (2021). Detecting fake news in social media using voting classifier. IEEE Access, 9, 161909–161925.
• Kumari, R., & Ekbal, A. (2021). Amfb: Attention based multimodal factorized bilinear pooling for multimodal fake news detection. Expert Systems with Applications, 184, 115412.
• di Domenico, N. F. T. M. (2021). O Uso de Inferência Variacional para Reconhecimento de Emoçoes em Música [PhD thesis]. Universidade Federal do Rio de Janeiro.
• Yuan, H., Zheng, J., Ye, Q., Qian, Y., & Zhang, Y. (2021). Improving fake news detection with domain-adversarial and graph-attention neural network. Decision Support Systems, 151, 113633.
• Zhang, C., Yu, J., & Chen, Z. (2021). Music emotion recognition based on combination of multiple features and neural network. 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 4, 1461–1465.
• Dao, M.-S., Zettsu, K., & Rage, U. K. (2021). Image-2-aqi: Aware of the surrounding air qualification by a few images. Advances and Trends in Artificial Intelligence. From Theory to Practice: 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26–29, 2021, Proceedings, Part II 34, 335–346.
• Li, P., Sun, X., Yu, H., Tian, Y., Yao, F., & Xu, G. (2021). Entity-oriented multi-modal alignment and fusion network for fake news detection. IEEE Transactions on Multimedia, 24, 3455–3468.
• Wu, Y., Zhan, P., Zhang, Y., Wang, L., & Xu, Z. (2021). Multimodal fusion with co-attention networks for fake news detection. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2560–2569.
• Hariharan, K., Lobo, A., & Deshmukh, S. (2021). Hybrid Approach for Effective Disaster Management Using Twitter Data and Image-Based Analysis. 2021 International Conference on Communication Information and Computing Technology (ICCICT), 1–6.
• Adão Teixeira Marcos Vinı́cius, & Avila, S. (2021). What should we pay attention to when classifying violent videos? Proceedings of the 16th International Conference on Availability, Reliability and Security. https://doi.org/10.1145/3465481.3470059
• Mohammed, S., Getahun, F., & Chbeir, R. (2021). 5W1H Aware Framework for Representing and Detecting Real Events from Multimedia Digital Ecosystem. European Conference on Advances in Databases and Information Systems, 57–70.
• Koutini, K., Eghbal-zadeh, H., & Widmer, G. (2021). Receptive Field Regularization Techniques for Audio Classification and Tagging With Deep Convolutional Neural Networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 1987–2000. https://doi.org/10.1109/TASLP.2021.3082307
• Leyva, R., & Sanchez, V. (2021). Video Memorability Prediction Via Late Fusion Of Deep Multi-Modal Features. 2021 IEEE International Conference on Image Processing (ICIP), 2488–2492. https://doi.org/10.1109/ICIP42928.2021.9506411
• Chen, J., Wu, Z., Yang, Z., Xie, H., Wang, F. L., & Liu, W. (2021). Multimodal Fusion Network with Latent Topic Memory for Rumor Detection. 2021 IEEE International Conference on Multimedia and Expo (ICME), 1–6. https://doi.org/10.1109/ICME51207.2021.9428404
• Knox, D., Greer, T., Ma, B., Kuo, E., Somandepalli, K., & Narayanan, S. (2021). Loss Function Approaches for Multi-label Music Tagging. 2021 International Conference on Content-Based Multimedia Indexing (CBMI), 1–4. https://doi.org/10.1109/CBMI50038.2021.9461913
• Yang, S.-wen, Chi, P.-H., Chuang, Y.-S., Lai, C.-I. J., Lakhotia, K., Lin, Y. Y., Liu, A. T., Shi, J., Chang, X., Lin, G.-T., Huang, T.-H., Tseng, W.-C., Lee, K.-tik, Liu, D.-R., Huang, Z., Dong, S., Li, S.-W., Watanabe, S., Mohamed, A., & Lee, H.-yi. (2021). SUPERB: Speech processing Universal PERformance Benchmark.
• Pustu-Iren, K., Müller-Budack, E., Hakimov, S., & Ewerth, R. (2021). Visualizing Copyright-Protected Video Archive Content Through Similarity Search. International Conference on Theory and Practice of Digital Libraries, 123–127.
• Yogapriya, J., Chandran, V., Sumithra, M. G., Anitha, P., Jenopaul, P., & Suresh Gnana Dhas, C. (2021). Gastrointestinal tract disease classification from wireless endoscopy images using pretrained deep learning model. Computational and Mathematical Methods in Medicine, 2021.
• MOHAMED, M. A. R. W. A. H. U. S. S. I. E. N., KHAFAGY, M. O. H. A. M. E. D. H. E. L. M. Y., & HASAN, M. O. H. A. M. E. D. (2021). MUSIC RECOMMENDATION SYSTEM USED EMOTIONS TO TRACK AND CHANGE NEGATIVE USERS’MOOD. Journal of Theoretical and Applied Information Technology, 99(17).
• Jiang, J., & Zhang, H. (2021). Research on the Method of Cross-modal Affective Association in Audiovisual. 2021 International Conference on Culture-Oriented Science & Technology (ICCST), 38–42. https://doi.org/10.1109/ICCST53801.2021.00019
• Jony, R. I., Woodley, A., & Perrin, D. (2021). Flood Detection in Social Media Using Multimodal Fusion on Multilingual Dataset. 2021 Digital Image Computing: Techniques and Applications (DICTA), 01–08. https://doi.org/10.1109/DICTA52665.2021.9647169
• Tirupattur, P., Schulze, C., & Dengel, A. (2021). Violence Detection in Videos. ArXiv Preprint ArXiv:2109.08941.
• Roy, A., & Ekbal, A. (2021). MulCoB-MulFaV: Multimodal Content Based Multilingual Fact Verification. 2021 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN52387.2021.9533916
• Godwin, T., Rizos, G., Baird, A., Al Futaisi, N. D., Brisse, V., & Schuller, B. W. (2021). Evaluating deep music generation methods using data augmentation. 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP), 1–6.
• Jaiswal, R., Singh, U. P., & Singh, K. P. (2021). Fake News Detection Using BERT-VGG19 Multimodal Variational Autoencoder. 2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), 1–5. https://doi.org/10.1109/UPCON52273.2021.9667614
• Brown, A., Coto, E., & Zisserman, A. (2021). Automated video labelling: Identifying faces by corroborative evidence. 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR), 77–83.
• Cheema, G. S., Hakimov, S., Müller-Budack, E., & Ewerth, R. (2021). On the role of images for analyzing claims in social media. ArXiv Preprint ArXiv:2103.09602.
• Tuan, N. M. D., & Minh, P. Q. N. (2021). Multimodal fusion with BERT and attention mechanism for fake news detection. 2021 RIVF International Conference on Computing and Communication Technologies (RIVF), 1–6.
• Sweeney, L., Healy, G., & Smeaton, A. F. (2021). The influence of audio on video memorability with an audio gestalt regulated video memorability system. 2021 International Conference on Content-Based Multimedia Indexing (CBMI), 1–6.
• Kordopatis-Zilos, G., Galopoulos, P., Papadopoulos, S., & Kompatsiaris, I. (2021). Leveraging efficientnet and contrastive learning for accurate global-scale location estimation. Proceedings of the 2021 International Conference on Multimedia Retrieval, 155–163.
• Hassan, S. Z., Ahmad, K., Riegler, M. A., Hicks, S., Conci, N., Halvorsen, P., & Al-Fuqaha, A. (2021). Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021. ArXiv Preprint ArXiv:2111.11471.
• Yange, T. S., Egbunu, C. O., Onyekware, O., Rufai, M. A., & Godwin, C. (2021). Violence Detection in Ranches Using Computer Vision and Convolution Neural Network. Journal of Computer Scine and Information Technology, 94–104.
• Elsaeed, E., Ouda, O., Elmogy, M. M., Atwan, A., & El-Daydamony, E. (2021). Detecting fake news in social media using voting classifier. IEEE Access, 9, 161909–161925.
• Tan, H. H. (2021). Semi-supervised music emotion recognition using noisy student training and harmonic pitch class profiles. ArXiv Preprint ArXiv:2112.00702.
• Wu, Z., Chen, J., Yang, Z., Xie, H., Wang, F. L., & Liu, W. (2021). Cross-modal attention network with orthogonal latent memory for rumor detection. Web Information Systems Engineering–WISE 2021: 22nd International Conference on Web Information Systems Engineering, WISE 2021, Melbourne, VIC, Australia, October 26–29, 2021, Proceedings, Part I 22, 527–541.
• Kiziltepe, R. S., Sweeney, L., Constantin, M. G., Doctor, F., Herrera Alba Garcı́a Seco, Demarty, C.-H., Healy, G., Ionescu, B., & Smeaton, A. F. (2021). An annotated video dataset for computing video memorability. Data in Brief, 39, 107671.
• Kiziltepe, R. S., Sweeney, L., Constantin, M. G., Doctor, F., Herrera Alba Garcı́a Seco, Demarty, C.-H., Healy, G., Ionescu, B., & Smeaton, A. F. (2021). An annotated video dataset for computing video memorability. Data in Brief, 39, 107671.
• Zhang, D., Nayak, R., & Bashar, M. A. (2021). Exploring fusion strategies in deep learning models for multi-modal classification. Australasian Conference on Data Mining, 102–117.
• Alonso-Bartolome, S., & Segura-Bedmar, I. (2021). Multimodal fake news detection. ArXiv Preprint ArXiv:2112.04831.
• Kiziltepe, R. S., Constantin, M. G., Demarty, C.-H., Healy, G., Fosco, C., Herrera Alba Garcı́a Seco, Halder, S., Ionescu, B., Matran-Fernandez, A., Smeaton, A. F., & others. (2021). Overview of the MediaEval 2021 predicting media memorability task. ArXiv Preprint ArXiv:2112.05982.
• Martin, P.-E. (2021). Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark. ArXiv Preprint ArXiv:2112.12074.
• Zahra, A., & Martin, P.-E. (2021). Two stream network for stroke detection in table tennis. ArXiv Preprint ArXiv:2112.12073.
• Sweeney, L., Healy, G., & Smeaton, A. F. (2021). Predicting media memorability: comparing visual, textual and auditory features. ArXiv Preprint ArXiv:2112.07969.
• El Vaigh, C. B., Girault, T., Mallart, C., & Nguyen, D. H. (2021). Detecting Fake News Conspiracies with Multitask and Prompt-Based Learning. MediaEval 2021-MediaEval Multimedia Evaluation Benchmark. Workshop, 1–3.
• Ahmad, K., Ayub, M. A., Ahmad, K., Al-Fuqaha, A., & Ahmad, N. (2021). Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content. ArXiv Preprint ArXiv:2112.12060.
• Zhou, Y., Gonzalez, A., Tabassum, P., & Tesic, J. (2021). DL-TXST NewsImages: Contextual Feature Enrichment for Image-Text Rematching. Proceedings of the MediaEval Benchmarking Initiative for Multimedia Evaluation.
• Shebaro, M., Oliver, J., Olarewaju, T., & Tesic, J. (2021). DL-TXST fake news: Enhancing tweet content classification with adapted language models. Working Notes Proceedings of the MediaEval 2021 Workshop, Online, 13–15.
• Coutinho, E., Alshukri, A., de Berardinis, J., & Dowrick, C. (2021). POLYHYMNIA Mood–Empowering people to cope with depression through music listening. Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, 188–193.
• Li, H., Bo, H., Ma, L., Wang, L., & Li, H. (2021). Music Emotion Recognition through Sparse Canonical Correlation Analysis. 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR), 354–359.
• Huang, J., He, R., Chen, J., Li, S., Deng, Y., & Wu, X. (2021). Boosting advanced nasopharyngeal carcinoma stage prediction using a Two-stage classification framework based on deep learning. International Journal of Computational Intelligence Systems, 14, 1–14.
• Martin, P.-E., Calandre, J., Mansencal, B., Benois-Pineau, J., Péteri, R., Mascarilla, L., & Morlier, J. (2021). Sports video: Fine-grained action detection and classification of table tennis strokes from videos for mediaeval 2021. ArXiv Preprint ArXiv:2112.11384.
• Tovstogan, P., Bogdanov, D., & Porter, A. (2021). Media-Eval 2021: Emotion and Theme Recognition in Music Using Jamendo. Proc. of the MediaEval 2021 Workshop, Online, 13–15.
• Claveau, V., Chaffin, A., & Kijak, E. (2021). Generating artificial texts as substitution or complement of training data. ArXiv Preprint ArXiv:2110.13016.
• Wang, H. (2021). Research on the application of wireless wearable sensing devices in interactive music. Journal of Sensors, 2021, 1–8.
• Bhattacharjee, S. D., & Yuan, J. (2021). Multimodal co-training for fake news identification using attention-aware fusion. Asian Conference on Pattern Recognition, 282–296.
• 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.
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• Wang, S., Wang, W., Chen, S., & Jin, Q. (2018). RUC at MediaEval 2018: Visual and Textual Features Exploration for Predicting Media Memorability. MediaEval Working Notes Proceedings.
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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.
• Hicks, S. A., Smedsrud, P. H., Halvorsen, P., & Riegler, M. (2018). Deep Learning Based Disease Detection Using Domain Specific Transfer Learning. MediaEval Working Notes Proceedings.
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• Sun, W., & Zhang, X. (2018). Video Memorability Prediction with Recurrent Neural Networks and Video Titles at the 2018 MediaEval Predicting Media Memorability Task. MediaEval Working Notes Proceedings.
• Nazary, F., & Deldjoo, Y. (2018). Movie Rating Prediction using Multimedia Content and Modeling as a Classification Problem. MediaEval Working Notes Proceedings.
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• Hanif, M., Tahir, M. A., & Rafi, M. (2018). Detection of passable roads using Ensemble of Global and Local. 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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• Tkachenko, N., Zubiaga, A., & Procter, R. (2017). Wisc at mediaeval 2017: Multimedia satellite task. MediaEval Working Notes Proceedings.
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• Zhang, X., Wang, S., Li, Z., & Ma, S. (2017). Landmark image retrieval by jointing feature refinement and multimodal classifier learning. IEEE Transactions on Cybernetics.
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• Le, N., Bredin, H., Sargent, G., Lopez-Otero, P., Barras, C., Guinaudeau, C., Gravier, G., da Fonseca, G. B., Freire, I. L., & others. (2017). Towards large scale multimedia indexing: A case study on person discovery in broadcast news. ACM International Workshop on Content-Based Multimedia Indexing.
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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.
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• Nogueira, K., Fadel, S. G., Dourado, Í. C., de Oliveira Werneck, R., Muñoz, J. A. V., Penatti, O. A. B., Calumby, R. T., Li, L., dos Santos, J. A., & da Silva Torres, R. (2017). Data-Driven Flood Detection using Neural Networks. MediaEval Working Notes Proceedings.
• Gündogdu, B. (2017). Keyword search for low resource languages.
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