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‎bib/index.html‎

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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 1142 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 1149 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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<ul class="bibliography"><li><span id="fong2025theory">Fong, H., Kumar, V., &amp; 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><span id="wang2025fake">Wang, X., Meng, J., Zhao, D., Meng, X., &amp; 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><span id="qiang2025mle">Qiang, R., Zhuang, Y., Singh, A., Liang, P., Zhang, C., Yang, S., &amp; 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><span id="pereira2024unsupervised">Pereira-Ferrero, V. H., Lewis, T. G., Valem, L. P., Ferrero, L. G. P., Pedronette, D. C. G., &amp; Latecki, L. J. (2024). Unsupervised affinity learning based on manifold analysis for image retrieval: A survey. <i>Computer Science Review</i>, <i>53</i>, 100657.</span></li>
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<li><span id="li2024multimodal">Li, Y., Jia, K., &amp; Wang, Q. (2024). Multimodal Fake News Detection Based on Contrastive Learning and Similarity Fusion. <i>IEEE Access</i>.</span></li>
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<li><span id="gaonkar2024exploring">Gaonkar, M. N., Thenkanidiyoor, V., Dinesh, D. A., &amp; Muralikrishna, H. (2024). Exploring the Effectiveness of Feature Reduction and Kernel-Based Matching for Query-by-Example Spoken Term Detection Using CNN. <i>IEEE Access</i>.</span></li>
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<li><span id="espinosa2024automatic">Espinosa, D. Y., Sidorov, G., &amp; Ricárdez-Vázquez, E. (2024). <i>Automatic Identification of Conspiracy Theories Using BERT</i>.</span></li>
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<li><span id="kirar2024multimodal">Kirar, R. K., &amp; Khan, N. R. (2024). Multimodal Fusion-Based Hybrid CRNN. <i>Universal Threats in Expert Applications and Solutions: Proceedings of 3rd UNI-TEAS 2024, Volume 1</i>, <i>1006</i>, 167.</span></li>
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<li><span id="wang2024augmenting">Wang, J.-H., Norouzi, M., &amp; Tsai, S. M. (2024). Augmenting multimodal content representation with transformers for misinformation detection. <i>Big Data and Cognitive Computing</i>, <i>8</i>(10), 134.</span></li>
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<li><span id="liu2024multimodal">Liu, Y., Wen, Z., Jin, M., Fan, D., Li, S., Liu, B., Jiang, J., &amp; Xiao, X. (2024). A Multimodal Fake News Detection Model with Self-supervised Unimodal Label Generation. <i>International Conference on Intelligent Computing</i>, 130–141.</span></li>
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<li><span id="chen2024advancing">Chen, Y., Ma, Z., Wang, M., &amp; Liu, M. (2024). Advancing Music Emotion Recognition: A Transformer Encoder-Based Approach. <i>Proceedings of the 6th ACM International Conference on Multimedia in Asia</i>, 1–5.</span></li>
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<li><span id="yin2024multimodal">Yin, Y., Yang, B., &amp; Li, C. (2024). Multimodal rumor detection based on co-attention mechanism. <i>2024 5th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)</i>, 265–270.</span></li>
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<li><span id="li2024understanding">Li, H., Bo, H., Ma, L., Chen, J., &amp; Li, H. (2024). Understanding Music and Emotion from the Brain. In <i>Artificial Intelligence for Art Creation and Understanding</i> (pp. 253–276). CRC Press.</span></li>

‎editions/2020/index.html‎

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‎editions/2020/tasks/fakenews/index.html‎

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‎editions/2020/tasks/floodmultimedia/index.html‎

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‎editions/2020/tasks/gamestory/index.html‎

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‎editions/2020/tasks/lifelogging/index.html‎

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‎editions/2020/tasks/medico/index.html‎

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‎editions/2020/tasks/memorability/index.html‎

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‎editions/2020/tasks/music/index.html‎

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