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<p>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 1163 papers that use data from MediaEval.</p>
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<p>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 1176 papers that use data from MediaEval.</p>
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<p>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.</p>
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<ulclass="bibliography"><li><spanid="YU2025104301">Yu, K., Jiao, S., & Ma, Z. (2025). Fake News Detection Based on BERT Multi-domain and Multi-modal Fusion Network. <i>Computer Vision and Image Understanding</i>, <i>252</i>, 104301. https://doi.org/https://doi.org/10.1016/j.cviu.2025.104301</span></li>
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<ulclass="bibliography"><li><spanid="watcharasupat2025uncertainty">Watcharasupat, K. N., Ding, Y., Ma, T. A., Seshadri, P., & Lerch, A. (2025). Uncertainty Estimation in the Real World: A Study on Music Emotion Recognition. <i>European Conference on Information Retrieval</i>, 218–232.</span></li>
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<li><spanid="kumar2025seeing">Kumar, P., Khandelwal, E., Tapaswi, M., & Sreekumar, V. (2025). Seeing Eye to AI: Comparing human gaze and model attention in video memorability. <i>2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)</i>, 2082–2091.</span></li>
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<li><spanid="kang2025towards">Kang, J., & Herremans, D. (2025). Towards Unified Music Emotion Recognition across Dimensional and Categorical Models. <i>ArXiv Preprint ArXiv:2502.03979</i>.</span></li>
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<li><spanid="YU2025104301">Yu, K., Jiao, S., & Ma, Z. (2025). Fake News Detection Based on BERT Multi-domain and Multi-modal Fusion Network. <i>Computer Vision and Image Understanding</i>, <i>252</i>, 104301. https://doi.org/https://doi.org/10.1016/j.cviu.2025.104301</span></li>
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<li><spanid="zheng-etal-2025-unveiling">Zheng, X., Luo, M., & Wang, X. (2025). Unveiling Fake News with Adversarial Arguments Generated by Multimodal Large Language Models. In O. Rambow, L. Wanner, M. Apidianaki, H. Al-Khalifa, B. D. Eugenio, & S. Schockaert (Eds.), <i>Proceedings of the 31st International Conference on Computational Linguistics</i> (pp. 7862–7869). Association for Computational Linguistics. https://aclanthology.org/2025.coling-main.526/</span></li>
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<li><spanid="flaccavento:hal-04876532">Flaccavento, A., Peskine, Y., Papotti, P., Torlone, R., & Troncy, R. (2025, January). Automated Detection of Tropes In Short Texts. <i>COLING 2025, 31st International Conference on Computational Linguistics</i>. https://hal.science/hal-04876532</span></li>
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<li><spanid="fong2025theory">Fong, H., Kumar, V., & Sudhir, K. (2025). A theory-based explainable deep learning architecture for music emotion. <i>Marketing Science</i>, <i>44</i>(1), 196–219.</span></li>
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<li><spanid="wang2025fake">Wang, X., Meng, J., Zhao, D., Meng, X., & Sun, H. (2025). Fake news detection based on multi-modal domain adaptation. <i>Neural Computing and Applications</i>, <i>37</i>(7), 5781–5793.</span></li>
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<li><spanid="qiang2025mle">Qiang, R., Zhuang, Y., Singh, A., Liang, P., Zhang, C., Yang, S., & Dai, B. (2025). MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline. <i>ArXiv Preprint ArXiv:2510.07307</i>.</span></li>
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<li><spanid="xie2024integrating">Xie, A., Zhu, F., Han, J., & Hu, S. (2024). Integrating open-domain knowledge via large language model for multimodal fake news detection. <i>2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)</i>, 1917–1922.</span></li>
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<li><spanid="hua2024impact">Hua, V., Nguyen, T., Dao, M.-S., Nguyen, H. D., & Nguyen, B. T. (2024). The impact of data imputation on air quality prediction problem. <i>Plos One</i>, <i>19</i>(9), e0306303.</span></li>
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<li><spanid="zhao2024robust">Zhao, Z., Alzubaidi, L., Zhang, J., Duan, Y., Naseem, U., & Gu, Y. (2024). Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging. <i>ArXiv Preprint ArXiv:2407.06566</i>.</span></li>
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<li><spanid="santhi2024flood">Santhi, V., Krishnamurthi, I., & Madhumitha, N. H. (2024). Flood Detection and Segmentation Using Deep Learning Models. <i>2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC)</i>, 226–231.</span></li>
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<li><spanid="cai2024sdcrnet">Cai, L., Gan, Y., Hu, J., & Liu, L. (2024). SDCRNet: A Lightweight Spatial Detail Fused Context Reconstruction Network for Real-time Polyp Segmentation. <i>Authorea Preprints</i>.</span></li>
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<li><spanid="bai2024learning">Bai, Y., Liu, Y., & Li, Y. (2024). Learning frequency-aware cross-modal interaction for multimodal fake news detection. <i>IEEE Transactions on Computational Social Systems</i>, <i>11</i>(5), 6568–6579.</span></li>
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<li><spanid="ljungar2024geo">Ljungar, A. (2024). <i>Geo-localization of caption-image pairs on social media in Russia and neighboring countries</i>.</span></li>
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<li><spanid="reineck2024importance">Reineck, S. (2024). <i>The Importance of Language Localization for Geolocalization: Geolocalization of Images and Text in China</i>.</span></li>
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<li><spanid="frobe2024reneuir">Fröbe, M., Mackenzie, J., Mitra, B., Nardini, F. M., & Potthast, M. (2024). ReNeuIR at SIGIR 2024: The third workshop on reaching efficiency in neural information retrieval. <i>Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval</i>, 3051–3054.</span></li>
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<li><spanid="ekbal2024dive">Ekbal, A., & Kumari, R. (2024). <i>Dive Into Misinformation Detection: From Unimodal to Multimodal and Multilingual Misinformation Detection</i> (Vol. 30). Springer Nature.</span></li>
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<li><spanid="kuntur2024under">Kuntur, S., Wróblewska, A., Paprzycki, M., & Ganzha, M. (2024). Under the influence: A survey of large language models in fake news detection. <i>IEEE Transactions on Artificial Intelligence</i>.</span></li>
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<li><spanid="zeng2024multimodal">Zeng, F., Li, W., Gao, W., & Pang, Y. (2024). Multimodal misinformation detection by learning from synthetic data with multimodal LLMs. <i>ArXiv Preprint ArXiv:2409.19656</i>.</span></li>
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<li><spanid="anggrainingsih2024transformer">Anggrainingsih, R., Hassan, G. M., & Datta, A. (2024). Transformer-based models for combating rumours on microblogging platforms: a review. <i>Artificial Intelligence Review</i>, <i>57</i>(8), 212.</span></li>
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