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@@ -199,7 +199,7 @@ <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 1077 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 1086 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>
 
@@ -218,6 +218,13 @@ <h2 class="subtitle">Bibliography</h2>
 <li><span id="raj2022arcnn">Raj, C., &amp; Meel, P. (2022). ARCNN framework for multimodal infodemic detection. <i>Neural Networks</i>, <i>146</i>, 36–68.</span></li>
 <li><span id="xiao2022survey">Xiao, K., Qian, Z., &amp; Qin, B. (2022). A survey of data representation for multi-modality event detection and evolution. <i>Applied Sciences</i>, <i>12</i>(4), 2204.</span></li>
 <li><span id="ali2022predicting">Ali, H., Gilani, S. O., Khan, M. J., Waris, A., Khattak, M. K., &amp; Jamil, M. (2022). Predicting Episodic Video Memorability Using Deep Features Fusion Strategy. <i>2022 IEEE/ACIS 20th International Conference on Software Engineering Research, Management and Applications (SERA)</i>, 39–46.</span></li>
+<li><span id="kumari2021amfb">Kumari, R., &amp; Ekbal, A. (2021). Amfb: Attention based multimodal factorized bilinear pooling for multimodal fake news detection. <i>Expert Systems with Applications</i>, <i>184</i>, 115412.</span></li>
+<li><span id="di2021uso">di Domenico, N. F. T. M. (2021). <i>O Uso de Inferência Variacional para Reconhecimento de Emoçoes em Música</i> [PhD thesis]. Universidade Federal do Rio de Janeiro.</span></li>
+<li><span id="yuan2021improving">Yuan, H., Zheng, J., Ye, Q., Qian, Y., &amp; Zhang, Y. (2021). Improving fake news detection with domain-adversarial and graph-attention neural network. <i>Decision Support Systems</i>, <i>151</i>, 113633.</span></li>
+<li><span id="zhang2021music">Zhang, C., Yu, J., &amp; Chen, Z. (2021). Music emotion recognition based on combination of multiple features and neural network. <i>2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)</i>, <i>4</i>, 1461–1465.</span></li>
+<li><span id="dao2021image">Dao, M.-S., Zettsu, K., &amp; Rage, U. K. (2021). Image-2-aqi: Aware of the surrounding air qualification by a few images. <i>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</i>, 335–346.</span></li>
+<li><span id="li2021entity">Li, P., Sun, X., Yu, H., Tian, Y., Yao, F., &amp; Xu, G. (2021). Entity-oriented multi-modal alignment and fusion network for fake news detection. <i>IEEE Transactions on Multimedia</i>, <i>24</i>, 3455–3468.</span></li>
+<li><span id="wu2021multimodal">Wu, Y., Zhan, P., Zhang, Y., Wang, L., &amp; Xu, Z. (2021). Multimodal fusion with co-attention networks for fake news detection. <i>Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021</i>, 2560–2569.</span></li>
 <li><span id="hariharan2021hybrid">Hariharan, K., Lobo, A., &amp; Deshmukh, S. (2021). Hybrid Approach for Effective Disaster Management Using Twitter Data and Image-Based Analysis. <i>2021 International Conference on Communication Information and Computing Technology (ICCICT)</i>, 1–6.</span></li>
 <li><span id="10.1145/3465481.3470059">Adão Teixeira Marcos Vinı́cius, &amp; Avila, S. (2021). What should we pay attention to when classifying violent videos? <i>Proceedings of the 16th International Conference on Availability, Reliability and Security</i>. https://doi.org/10.1145/3465481.3470059</span></li>
 <li><span id="mohammed20215w1h">Mohammed, S., Getahun, F., &amp; Chbeir, R. (2021). 5W1H Aware Framework for Representing and Detecting Real Events from Multimedia Digital Ecosystem. <i>European Conference on Advances in Databases and Information Systems</i>, 57–70.</span></li>
@@ -1278,6 +1285,8 @@ <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="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>
 <li><span id="jony2020fusing">Jony, R. I., Woodley, A., &amp; Perrin, D. Fusing Visual Features and Metadata to Detect Flooding in Flickr Images. <i>2020 Digital Image Computing: Techniques and Applications (DICTA)</i>, 1–8.</span></li></ul>
 

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