This work is licensed under the Creative Commons Attribution 4.0 International License.
Chiorrini, A., Diamantini, C., & Storti, P. E. (2023). An emotion-aware search engine for multimedia content based on deep learning algorithms. International Journal of Computer Applications in Technology, 73(2), 130-139.ChiorriniA.DiamantiniC.StortiP. E. (2023). An emotion-aware search engine for multimedia content based on deep learning algorithms. International Journal of Computer Applications in Technology, 73(2), 130-139.Search in Google Scholar
Sun, L., Lian, Z., & Tao, L. J. (2024). Efficient multimodal transformer with dual-level feature restoration for robust multimodal sentiment analysis. IEEE Transactions on Affective Computing, 15(1), 309-325.SunL.LianZ.TaoL. J. (2024). Efficient multimodal transformer with dual-level feature restoration for robust multimodal sentiment analysis. IEEE Transactions on Affective Computing, 15(1), 309-325.Search in Google Scholar
Mai, S., Zeng, Y., & Hu, Z. H. (2023). Hybrid contrastive learning of tri-modal representation for multimodal sentiment analysis. IEEE Transactions on Affective Computing, 14(3), 2276-2289.MaiS.ZengY.HuZ. H. (2023). Hybrid contrastive learning of tri-modal representation for multimodal sentiment analysis. IEEE Transactions on Affective Computing, 14(3), 2276-2289.Search in Google Scholar
Stappen, L., Baird, A., & Schuller, S. B. (2023). The multimodal sentiment analysis in car reviews (MUSE-CAR) dataset: Collection, insights and improvements. IEEE Transactions on Affective Computing, 14(2), 1334-1350.StappenL.BairdA.SchullerS. B. (2023). The multimodal sentiment analysis in car reviews (MUSE-CAR) dataset: Collection, insights and improvements. IEEE Transactions on Affective Computing, 14(2), 1334-1350.Search in Google Scholar
Li, M., Zhu, Y., Gao, W., Cao, M., & Wang, S. (2020). Joint sentiment part topic regression model for multimodal analysis. Information (Switzerland), 11(10), 486.LiM.ZhuY.GaoW.CaoM.WangS. (2020). Joint sentiment part topic regression model for multimodal analysis. Information (Switzerland), 11(10), 486.Search in Google Scholar
Gkoumas, D., Li, Q., Lioma, C., Yu, Y., & Song, D. (2021). What makes the difference? An empirical comparison of fusion strategies for multimodal language analysis. Information Fusion, 66(6), 184-197.GkoumasD.LiQ.LiomaC.YuY.SongD. (2021). What makes the difference? An empirical comparison of fusion strategies for multimodal language analysis. Information Fusion, 66(6), 184-197.Search in Google Scholar
Huang, Q., Chen, J., Huang, C., Huang, X., & Wang, Y. (2024). Text-centered cross-sample fusion network for multimodal sentiment analysis. Multimedia Systems, 30(4), 1-19.HuangQ.ChenJ.HuangC.HuangX.WangY. (2024). Text-centered cross-sample fusion network for multimodal sentiment analysis. Multimedia Systems, 30(4), 1-19.Search in Google Scholar
Guo, X., Tian, S., Yu, L., & He, X. (2024). SmarTran: Smart routing attention network for multimodal sentiment analysis. Applied Intelligence, 54(24), 12742-12763.GuoX.TianS.YuL.HeX. (2024). SmarTran: Smart routing attention network for multimodal sentiment analysis. Applied Intelligence, 54(24), 12742-12763.Search in Google Scholar
Sun, H., Chen, Y. W., & Lin, L. (2023). Tensorformer: A tensor-based multimodal transformer for multimodal sentiment analysis and depression detection. IEEE Transactions on Affective Computing, 14(4), 2776-2786.SunH.ChenY. W.LinL. (2023). Tensorformer: A tensor-based multimodal transformer for multimodal sentiment analysis and depression detection. IEEE Transactions on Affective Computing, 14(4), 2776-2786.Search in Google Scholar
Parseh, M. J., Rahmanimanesh, M., Keshavarzi, P., & Azimifar, Z. (2022). Semantic embedding: Scene image classification using scene-specific objects. Multimedia Systems, 29(2), 669-691.ParsehM. J.RahmanimaneshM.KeshavarziP.AzimifarZ. (2022). Semantic embedding: Scene image classification using scene-specific objects. Multimedia Systems, 29(2), 669-691.Search in Google Scholar
Qi, Q., Lin, L., & Zhang, R. (2021). Feature extraction network with attention mechanism for data enhancement and recombination fusion for multimodal sentiment analysis. Information (Switzerland), 12(9), 342.QiQ.LinL.ZhangR. (2021). Feature extraction network with attention mechanism for data enhancement and recombination fusion for multimodal sentiment analysis. Information (Switzerland), 12(9), 342.Search in Google Scholar
Saha, T., Upadhyaya, A., & Bhattacharyya, S. P. (2022). A multitask multimodal ensemble model for sentiment-and emotion-aided tweet act classification. IEEE Transactions on Computational Social Systems, 9(2), 508-517.SahaT.UpadhyayaA.BhattacharyyaS. P. (2022). A multitask multimodal ensemble model for sentiment-and emotion-aided tweet act classification. IEEE Transactions on Computational Social Systems, 9(2), 508-517.Search in Google Scholar
Roldo, L., De Charette, R., & Verroust-Blondet, A. (2022). 3D semantic scene completion: A survey. International Journal of Computer Vision, 130(8), 1978-2005.RoldoL.De CharetteR.Verroust-BlondetA. (2022). 3D semantic scene completion: A survey. International Journal of Computer Vision, 130(8), 1978-2005.Search in Google Scholar
Du, Y., Xie, R., Zhang, B., & Yin, Z. (2024). FMCF: Few-shot multimodal aspect-based sentiment analysis framework based on contrastive finetuning. Applied Intelligence, 54(24), 12629-12643.DuY.XieR.ZhangB.YinZ. (2024). FMCF: Few-shot multimodal aspect-based sentiment analysis framework based on contrastive finetuning. Applied Intelligence, 54(24), 12629-12643.Search in Google Scholar
Zhang, Q., Shi, L., Liu, P., Zhu, Z., & Xu, L. (2022). Retracted article: ICDN: Integrating consistency and difference networks by transformer for multimodal sentiment analysis. Applied Intelligence, 53(12), 16332-16345.ZhangQ.ShiL.LiuP.ZhuZ.XuL. (2022). Retracted article: ICDN: Integrating consistency and difference networks by transformer for multimodal sentiment analysis. Applied Intelligence, 53(12), 16332-16345.Search in Google Scholar
Zhong, J. I., Chen, K., Yuqing, H. E., Pang, Y., & Xuelong, L. I. (2022). Heterogeneous memory enhanced graph reasoning network for cross-modal retrieval. Science China Information Sciences, 65(7), 1-13.ZhongJ. I.ChenK.YuqingH. E.PangY.XuelongL. I. (2022). Heterogeneous memory enhanced graph reasoning network for cross-modal retrieval. Science China Information Sciences, 65(7), 1-13.Search in Google Scholar
Best, W., Hughes, L., Masterson, J., Thomas, M. S. C., & Shobbrook, K. (2021). Understanding differing outcomes from semantic and phonological interventions with children with word-finding difficulties: A group and case series study. Cortex, 134(3), 145-161.BestW.HughesL.MastersonJ.ThomasM. S. C.ShobbrookK. (2021). Understanding differing outcomes from semantic and phonological interventions with children with word-finding difficulties: A group and case series study. Cortex, 134(3), 145-161.Search in Google Scholar
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., & Jürgen Gall, et al. (2021). Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI dataset. The International Journal of Robotics Research, 40(8-9), 959-967.BehleyJ.GarbadeM.MiliotoA.QuenzelJ.BehnkeS.JürgenGall (2021). Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI dataset. The International Journal of Robotics Research, 40(8-9), 959-967.Search in Google Scholar
Dar, S., Liebenthal, E., Pan, H., Smith, T., & Stern, E. (2021). Abnormal semantic processing of threat words associated with excitement and hostility symptoms in schizophrenia. Schizophrenia Research, 228(6684), 394-402.DarS.LiebenthalE.PanH.SmithT.SternE. (2021). Abnormal semantic processing of threat words associated with excitement and hostility symptoms in schizophrenia. Schizophrenia Research, 228(6684), 394-402.Search in Google Scholar
Vo, A. D., Nguyen, Q. P., & Ock, C. Y. (2020). Semantic and syntactic analysis in learning representation based on a sentiment analysis model. Applied Intelligence, 50(3), 663-680.VoA. D.NguyenQ. P.OckC. Y. (2020). Semantic and syntactic analysis in learning representation based on a sentiment analysis model. Applied Intelligence, 50(3), 663-680.Search in Google Scholar
Xu, M., Zhong, Y., & Liu, S. Y. (2023). Multi-scale multi-temporal landscape pattern analysis using high spatial urban scene classification. International Journal of Remote Sensing, 44(23-24), 7570-7597.XuM.ZhongY.LiuS. Y. (2023). Multi-scale multi-temporal landscape pattern analysis using high spatial urban scene classification. International Journal of Remote Sensing, 44(23-24), 7570-7597.Search in Google Scholar
Cowen, A. S., & Keltner, D. (2021). Semantic space theory: A computational approach to emotion. Trends in Cognitive Sciences, 25(2), 124-136.CowenA. S.KeltnerD. (2021). Semantic space theory: A computational approach to emotion. Trends in Cognitive Sciences, 25(2), 124-136.Search in Google Scholar
Dai, D., Sakaridis, C., Hecker, S., & Gool, L. V. (2020). Curriculum model adaptation with synthetic and real data for semantic foggy scene understanding. International Journal of Computer Vision, 128(5), 1182-1204.DaiD.SakaridisC.HeckerS.GoolL. V. (2020). Curriculum model adaptation with synthetic and real data for semantic foggy scene understanding. International Journal of Computer Vision, 128(5), 1182-1204.Search in Google Scholar