deploy: d6da86e

Author: cmihaigabriel

bib/index.html

@@ -207,13 +207,18 @@ <h2 class="subtitle">Bibliography</h2>
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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>
+	<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 1155 papers that use data from MediaEval.</p>
 
 <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>
 
 <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>
 <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>
 <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>
+<li><span id="shan2024fake">Shan, F., Liu, M., Zhang, M., &amp; Wang, Z. (2024). Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network. <i>Computers, Materials &amp; Continua</i>, <i>80</i>(1).</span></li>
+<li><span id="oak2024novel">Oak, O., Nazre, R., Naigaonkar, S., Sawant, S., &amp; Joshi, A. (2024). A novel transfer learning based cnn model for wildfire susceptibility prediction. <i>2024 5th International Conference for Emerging Technology (INCET)</i>, 1–6.</span></li>
+<li><span id="fraile2024automatic">Fraile-Hernandez, J. M., Peńas, A., &amp; Moral, P. (2024). Automatic Identification of Narratives: Evaluation Framework, Annotation Methodology, and Dataset Creation. <i>IEEE Access</i>, <i>13</i>, 11734–11753.</span></li>
+<li><span id="liu2024rumor">Liu, Y., &amp; Li, X. (2024). Rumor Detection Mechanism for Multi-Modal Information in Social Media. <i>2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)</i>, 1–5.</span></li>
+<li><span id="byju2024generative">Byju, A., Ladwa, A. S., Sweeney, L., &amp; Smeaton, A. F. (2024). Generative Outpainting To Enhance the Memorability of Short-Form Videos. <i>ArXiv Preprint ArXiv:2411.14213</i>.</span></li>
 <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>
 <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>
 <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>
@@ -1356,6 +1361,7 @@ <h2 class="subtitle">Bibliography</h2>
 <li><span id="mediaeval00085">Alink, W., &amp; Cornacchia, R. (2011). Out-of-the-box strategy for Rich Speech Retrieval MediaEval 2011. <i>MediaEval Working Notes Proceedings</i>.</span></li>
 <li><span id="mediaeval00086">Eskevich, M., &amp; Jones, G. J. F. (2011). DCU at MediaEval 2011: Rich Speech Retrieval (RSR). <i>MediaEval Working Notes Proceedings</i>.</span></li>
 <li><span id="mediaeval00087">Ionescu, B., Seyerlehner, K., Vertan, C., &amp; Lambert, P. (2011). Audio-Visual content description for video genre classification in the context of social media. <i>MediaEval Working Notes Proceedings</i>.</span></li>
+<li><span id="kimhierarchical">Kim, Y. S., Jang, J. W., &amp; Kim, C. S. <i>Hierarchical Image Geolocalization Using ViT-based Encoders and Satellite Map Images</i>.</span></li>
 <li><span id="bassmandetecting">Bassman, T. J., Hanif, U., &amp; Xia, E. <i>Detecting Flooding in Social Media Imagery Using Multimodal Deep Learning</i>.</span></li>
 <li><span id="ahmadmodified">Ahmad, A., Badshah, N., &amp; Hassan, M. U. <i>A Modified Memory-Efficient U-Net for Segmentation of Polyps</i>.</span></li>
 <li><span id="scalespredicting">Scales, L. <i>Predicting Short-term Media Memorability from Captions</i>.</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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