Articoli di riviste sul tema "Deep multi-Modal learning"
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Shetty D S, Radhika. "Multi-Modal Fusion Techniques in Deep Learning". International Journal of Science and Research (IJSR) 12, n. 9 (5 settembre 2023): 526–32. http://dx.doi.org/10.21275/sr23905100554.
Roostaiyan, Seyed Mahdi, Ehsan Imani e Mahdieh Soleymani Baghshah. "Multi-modal deep distance metric learning". Intelligent Data Analysis 21, n. 6 (15 novembre 2017): 1351–69. http://dx.doi.org/10.3233/ida-163196.
Zhu, Xinghui, Liewu Cai, Zhuoyang Zou e Lei Zhu. "Deep Multi-Semantic Fusion-Based Cross-Modal Hashing". Mathematics 10, n. 3 (29 gennaio 2022): 430. http://dx.doi.org/10.3390/math10030430.
Du, Lin, Xiong You, Ke Li, Liqiu Meng, Gong Cheng, Liyang Xiong e Guangxia Wang. "Multi-modal deep learning for landform recognition". ISPRS Journal of Photogrammetry and Remote Sensing 158 (dicembre 2019): 63–75. http://dx.doi.org/10.1016/j.isprsjprs.2019.09.018.
Wang, Wei, Xiaoyan Yang, Beng Chin Ooi, Dongxiang Zhang e Yueting Zhuang. "Effective deep learning-based multi-modal retrieval". VLDB Journal 25, n. 1 (19 luglio 2015): 79–101. http://dx.doi.org/10.1007/s00778-015-0391-4.
Jeong, Changhoon, Sung-Eun Jang, Sanghyuck Na e Juntae Kim. "Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications". Data 4, n. 4 (12 ottobre 2019): 139. http://dx.doi.org/10.3390/data4040139.
Yang, Yang, Yi-Feng Wu, De-Chuan Zhan, Zhi-Bin Liu e Yuan Jiang. "Deep Robust Unsupervised Multi-Modal Network". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 luglio 2019): 5652–59. http://dx.doi.org/10.1609/aaai.v33i01.33015652.
Hua, Yan, Yingyun Yang e Jianhe Du. "Deep Multi-Modal Metric Learning with Multi-Scale Correlation for Image-Text Retrieval". Electronics 9, n. 3 (10 marzo 2020): 466. http://dx.doi.org/10.3390/electronics9030466.
Han, Dong, Hong Nie, Jinbao Chen, Meng Chen, Zhen Deng e Jianwei Zhang. "Multi-modal haptic image recognition based on deep learning". Sensor Review 38, n. 4 (17 settembre 2018): 486–93. http://dx.doi.org/10.1108/sr-08-2017-0160.
Pyrovolakis, Konstantinos, Paraskevi Tzouveli e Giorgos Stamou. "Multi-Modal Song Mood Detection with Deep Learning". Sensors 22, n. 3 (29 gennaio 2022): 1065. http://dx.doi.org/10.3390/s22031065.
Priyasad, Darshana, Tharindu Fernando, Simon Denman, Sridha Sridharan e Clinton Fookes. "Memory based fusion for multi-modal deep learning". Information Fusion 67 (marzo 2021): 136–46. http://dx.doi.org/10.1016/j.inffus.2020.10.005.
Kasa, Kevin, David Burns, Mitchell G. Goldenberg, Omar Selim, Cari Whyne e Michael Hardisty. "Multi-Modal Deep Learning for Assessing Surgeon Technical Skill". Sensors 22, n. 19 (27 settembre 2022): 7328. http://dx.doi.org/10.3390/s22197328.
Niu, Yulei, Zhiwu Lu, Ji-Rong Wen, Tao Xiang e Shih-Fu Chang. "Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation". IEEE Transactions on Image Processing 28, n. 4 (aprile 2019): 1720–31. http://dx.doi.org/10.1109/tip.2018.2881928.
Wang, Qiuli, Dan Yang, Zhihuan Li, Xiaohong Zhang e Chen Liu. "Deep Regression via Multi-Channel Multi-Modal Learning for Pneumonia Screening". IEEE Access 8 (2020): 78530–41. http://dx.doi.org/10.1109/access.2020.2990423.
Park, Jongchan, Min-Hyun Kim e Dong-Geol Choi. "Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection". Electronics 10, n. 7 (28 marzo 2021): 800. http://dx.doi.org/10.3390/electronics10070800.
Dong, Guan-Nan, Chi-Man Pun e Zheng Zhang. "Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation". IEEE Transactions on Information Forensics and Security 16 (2021): 4197–210. http://dx.doi.org/10.1109/tifs.2021.3098165.
Bhatt, Gaurav, Piyush Jha e Balasubramanian Raman. "Representation learning using step-based deep multi-modal autoencoders". Pattern Recognition 95 (novembre 2019): 12–23. http://dx.doi.org/10.1016/j.patcog.2019.05.032.
Belfedhal, Alaa Eddine. "Multi-Modal Deep Learning for Effective Malicious Webpage Detection". Revue d'Intelligence Artificielle 37, n. 4 (31 agosto 2023): 1005–13. http://dx.doi.org/10.18280/ria.370422.
M. Shahzad, H., Sohail Masood Bhatti, Arfan Jaffar e Muhammad Rashid. "A Multi-Modal Deep Learning Approach for Emotion Recognition". Intelligent Automation & Soft Computing 36, n. 2 (2023): 1561–70. http://dx.doi.org/10.32604/iasc.2023.032525.
Zhang, Ning, Huarui Wu, Huaji Zhu, Ying Deng e Xiao Han. "Tomato Disease Classification and Identification Method Based on Multimodal Fusion Deep Learning". Agriculture 12, n. 12 (25 novembre 2022): 2014. http://dx.doi.org/10.3390/agriculture12122014.
Kiela, Douwe, e Stephen Clark. "Learning Neural Audio Embeddings for Grounding Semantics in Auditory Perception". Journal of Artificial Intelligence Research 60 (26 dicembre 2017): 1003–30. http://dx.doi.org/10.1613/jair.5665.
Yang, Yang, Zhilei Wu, Yuexiang Yang, Shuangshuang Lian, Fengjie Guo e Zhiwei Wang. "A Survey of Information Extraction Based on Deep Learning". Applied Sciences 12, n. 19 (27 settembre 2022): 9691. http://dx.doi.org/10.3390/app12199691.
Li, Zhe, Yuming Jiang e Ruijiang Li. "Abstract 2313: Multi-modal deep learning to predict cancer outcomes by integrating radiology and pathology images". Cancer Research 84, n. 6_Supplement (22 marzo 2024): 2313. http://dx.doi.org/10.1158/1538-7445.am2024-2313.
Ghoniem, Rania M., Abeer D. Algarni, Basel Refky e Ahmed A. Ewees. "Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis". Symmetry 13, n. 4 (10 aprile 2021): 643. http://dx.doi.org/10.3390/sym13040643.
Li, Xuefei, Liangtu Song, Liu Liu e Linli Zhou. "GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users". Sensors 21, n. 21 (22 ottobre 2021): 7010. http://dx.doi.org/10.3390/s21217010.
Zheng, Qiushuo, Hao Wen, Meng Wang e Guilin Qi. "Visual Entity Linking via Multi-modal Learning". Data Intelligence 4, n. 1 (2022): 1–19. http://dx.doi.org/10.1162/dint_a_00114.
Wilson, Justin C., Suku Nair, Sandro Scielzo e Eric C. Larson. "Objective Measures of Cognitive Load Using Deep Multi-Modal Learning". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, n. 1 (19 marzo 2021): 1–35. http://dx.doi.org/10.1145/3448111.
Sharma, Pulkit, Achut Manandhar, Patrick Thomson, Jacob Katuva, Robert Hope e David A. Clifton. "Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning". Sustainability 11, n. 22 (11 novembre 2019): 6312. http://dx.doi.org/10.3390/su11226312.
Glavan, Andreea, e Estefanía Talavera. "InstaIndoor and multi-modal deep learning for indoor scene recognition". Neural Computing and Applications 34, n. 9 (22 gennaio 2022): 6861–77. http://dx.doi.org/10.1007/s00521-021-06781-2.
Liu, Yu, e Tinne Tuytelaars. "A Deep Multi-Modal Explanation Model for Zero-Shot Learning". IEEE Transactions on Image Processing 29 (2020): 4788–803. http://dx.doi.org/10.1109/tip.2020.2975980.
Xiang, Lei, Yong Chen, Weitang Chang, Yiqiang Zhan, Weili Lin, Qian Wang e Dinggang Shen. "Deep-Learning-Based Multi-Modal Fusion for Fast MR Reconstruction". IEEE Transactions on Biomedical Engineering 66, n. 7 (luglio 2019): 2105–14. http://dx.doi.org/10.1109/tbme.2018.2883958.
Xu, Xiangyang, Yuncheng Li, Gangshan Wu e Jiebo Luo. "Multi-modal deep feature learning for RGB-D object detection". Pattern Recognition 72 (dicembre 2017): 300–313. http://dx.doi.org/10.1016/j.patcog.2017.07.026.
Wei, Jie, Huaping Liu, Gaowei Yan e Fuchun Sun. "Robotic grasping recognition using multi-modal deep extreme learning machine". Multidimensional Systems and Signal Processing 28, n. 3 (3 marzo 2016): 817–33. http://dx.doi.org/10.1007/s11045-016-0389-0.
Kim, Woo-Hyeon, Geon-Woo Kim e Joo-Chang Kim. "Multi-Modal Deep Learning based Metadata Extensions for Video Clipping". International Journal on Advanced Science, Engineering and Information Technology 14, n. 1 (28 febbraio 2024): 375–80. http://dx.doi.org/10.18517/ijaseit.14.1.19047.
Althenayan, Albatoul S., Shada A. AlSalamah, Sherin Aly, Thamer Nouh, Bassam Mahboub, Laila Salameh, Metab Alkubeyyer e Abdulrahman Mirza. "COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal". Sensors 24, n. 8 (20 aprile 2024): 2641. http://dx.doi.org/10.3390/s24082641.
Siddanna, S. R., e Y. C. Kiran. "Two Stage Multi Modal Deep Learning Kannada Character Recognition Model Adaptive to Discriminative Patterns of Kannada Characters". Indian Journal Of Science And Technology 16, n. 3 (22 gennaio 2023): 155–66. http://dx.doi.org/10.17485/ijst/v16i3.1904.
Williams-Lekuona, Mikel, Georgina Cosma e Iain Phillips. "A Framework for Enabling Unpaired Multi-Modal Learning for Deep Cross-Modal Hashing Retrieval". Journal of Imaging 8, n. 12 (15 dicembre 2022): 328. http://dx.doi.org/10.3390/jimaging8120328.
Zheng, Ke, e Zhou Li. "An Image-Text Matching Method for Multi-Modal Robots". Journal of Organizational and End User Computing 36, n. 1 (8 dicembre 2023): 1–21. http://dx.doi.org/10.4018/joeuc.334701.
Juan, Bao, Tuo Min, Hou Meng Ting, Li Xi Yu e Wang Qun. "Research on Intelligent Medical Engineering Analysis and Decision Based on Deep Learning". International Journal of Web Services Research 19, n. 1 (1 gennaio 2022): 1–9. http://dx.doi.org/10.4018/ijwsr.314949.
Song, Kuiyong, Lianke Zhou e Hongbin Wang. "Deep Coupling Recurrent Auto-Encoder with Multi-Modal EEG and EOG for Vigilance Estimation". Entropy 23, n. 10 (9 ottobre 2021): 1316. http://dx.doi.org/10.3390/e23101316.
D’Isanto, A. "Uncertain Photometric Redshifts with Deep Learning Methods". Proceedings of the International Astronomical Union 12, S325 (ottobre 2016): 209–12. http://dx.doi.org/10.1017/s1743921316013090.
Liang, Chengxu, e Jianshe Dong. "A Survey of Deep Learning-based Facial Expression Recognition Research". Frontiers in Computing and Intelligent Systems 5, n. 2 (1 settembre 2023): 56–60. http://dx.doi.org/10.54097/fcis.v5i2.12445.
Choi, Sanghyuk Roy, e Minhyeok Lee. "Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes". Biology 11, n. 10 (5 ottobre 2022): 1462. http://dx.doi.org/10.3390/biology11101462.
He, Chao, Xinghua Zhang, Dongqing Song, Yingshan Shen, Chengjie Mao, Huosheng Wen, Dingju Zhu e Lihua Cai. "Mixture of Attention Variants for Modal Fusion in Multi-Modal Sentiment Analysis". Big Data and Cognitive Computing 8, n. 2 (29 gennaio 2024): 14. http://dx.doi.org/10.3390/bdcc8020014.
Zhang, Huan, e Shunren Xia. "Enhancing Acute Bilirubin Encephalopathy Diagnosis with Multi-Modal MRI: A Deep Learning Approach". Applied Sciences 14, n. 6 (14 marzo 2024): 2464. http://dx.doi.org/10.3390/app14062464.
Farahnakian, Fahimeh, e Jukka Heikkonen. "Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection". Remote Sensing 12, n. 16 (5 agosto 2020): 2509. http://dx.doi.org/10.3390/rs12162509.
Hssayeni, Murtadha D., e Behnaz Ghoraani. "Multi-Modal Physiological Data Fusion for Affect Estimation Using Deep Learning". IEEE Access 9 (2021): 21642–52. http://dx.doi.org/10.1109/access.2021.3055933.
Arya, Nikhilanand, e Sriparna Saha. "Multi-modal advanced deep learning architectures for breast cancer survival prediction". Knowledge-Based Systems 221 (giugno 2021): 106965. http://dx.doi.org/10.1016/j.knosys.2021.106965.
YAO Hong-ge, 姚红革, 沈新霞 SHEN Xin-xia, 李宇 LI Yu, 喻钧 YU Jun e 雷松泽 LEI Song-ze. "Multi-modal Fusion Brain Tumor Detection Method Based on Deep Learning". ACTA PHOTONICA SINICA 48, n. 7 (2019): 717001. http://dx.doi.org/10.3788/gzxb20194807.0717001.
Паршин, А. И., М. Н. Аралов, В. Ф. Барабанов e Н. И. Гребенникова. "RANDOM MULTI-MODAL DEEP LEARNING IN THE PROBLEM OF IMAGE RECOGNITION". ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, n. 4 (20 ottobre 2021): 21–26. http://dx.doi.org/10.36622/vstu.2021.17.4.003.