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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 1136 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 1142 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="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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<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="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>
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<li><span id="wang2024multi">Wang, B., &amp; Zhang, S. (2024). A Multi-Reading Habits Fusion Adversarial Network for Multi-Modal Fake News Detection. <i>International Journal of Advanced Computer Science &amp; Applications</i>, <i>15</i>(7).</span></li>
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<li><span id="yadav2023etma">Yadav, A., Gaba, S., Khan, H., Budhiraja, I., Singh, A., &amp; Singh, K. K. (2023). ETMA: Efficient Transformer-Based Multilevel Attention Framework for Multimodal Fake News Detection. <i>IEEE Transactions on Computational Social Systems</i>.</span></li>
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<li><span id="chen2023multi">Chen, J., Jia, C., Zheng, H., Chen, R., &amp; Fu, C. (2023). Is multi-modal necessarily better? Robustness evaluation of multi-modal fake news detection. <i>IEEE Transactions on Network Science and Engineering</i>.</span></li>
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<li><span id="ayoughi2023self">Ayoughi, M., Mettes, P., &amp; Groth, P. (2023). Self-Contained Entity Discovery from Captioned Videos. <i>ACM Transactions on Multimedia Computing, Communications and Applications</i>, <i>19</i>(5s), 1–21.</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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