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Media (Intel Video Processing Library, Intel Media SDK) - Intel
Yet to speak with him about that,” iNTeLL said when asked if U-God, his father, approves of his latest venture. “He’s supportive of everything I do but with the ‘2nd Generation Wu’ stuff because it’s only been a few days since the single drop, I’m not sure if he’s heard it yet.”PXWER, son of Method Man played the track for his father who has been “in his corner” through this venture.“He’s supportive and he’s just letting me do what I do," PXWER said. ““I take serious pride in being a child of Wu-Tang but we’re also trying to cement ourselves in our careers.”“2nd Generation Wu” members say they will release a music video to their debut single by the end of 2019 and plan to drop an album in the “near future." “7 O.D,” produced by Jo-Jo Pellegrino and J.Glaze, racked up 29,000 views in three days.“We comin’ in strong,” iNTeLL said. “This isn’t a ‘dip our toe in’ kind of thing. This plan is in full force, we here.”If you purchase a product or register for an account through a link on our site, we may receive compensation. By using this site, you consent to our User Agreement and agree that your clicks, interactions, and personal information may be collected, recorded, and/or stored by us and social media and other third-party partners in accordance with our Privacy Policy. "Young Dirty Bastard," iNTeLL and PXWER are the children of "Old Dirty Bastard," U-God and Method Man, who founded the legendary hip-hop group Wu-Tang Clan. (Photo by Lebanese Ether)By Victoria Priola, eCommerce writerSTATEN ISLAND, N.Y.-- Wu-Tang Clan is for the children. Even their children know that.Sons of the legendary hip-hop group’s founding members came together to create “2nd Generation Wu,” a music group consisting of iNTeLL, the son of U-God; PXWER, son of Method Man; SUN GOD, son of Ghostface Killah and “Young Dirty Bastard,” son of the late “Old Dirty Bastard.”“Growing up, I’ve always had the mindset of 'I am who I am, my father is who he is,” iNTeLL told SILive.com. “We’re all next in line to receive ‘Wu-Tang is forever’ but at the same time, we all have our own unique sound and we’re ready to share that together.”The group dropped its debut single this month titled “7 O.D.” through Dock Street Records, based inside Amendment 18 in Stapleton. The opening lyric to the debut single is “God made everything around me; Forget about the money; None of it is real y’all.” (The video below has some choice language that may not be suitable for audiences of all ages. Viewers digression is advised.) “2nd Generation Wu" dropped their single around the 26th anniversary of Wu-Tang Clan’s praised album “Enter the Wu-Tang.”A HIP-HOP FAMILY TREESince the early 90′s when members of Wu-Tang Clan found themselves rapping in the streets of Shaolin, a familial connection was created and passed on to the lives of their children.------------------------------------------------------------------------Watch “Wu-Tang: An American Saga” on Hulu------------------------------------------------------------------------iNTeLL and PXWER are blood related through their mothers. The pair spent their lives pursuing music through Staten Island events and New York City venues. Over the course of 10 years, connections to the offspring of the other Wu-Tang Clan members began to form and the group -- 2nd Generation Wu" -- officially made their debut at a local hip-hop show on the stage of Amendment 18 in July.“Once I was aware that we all do music, I was like ‘holy s***,'” iNTeLL said. “We aren’t really in communication like that but we all have music in our DNA. Now imagine if we all get together. How powerful that would be.”The group wants to make one thing clear: They are not a cover band of their fathers. “2nd Generation” members honor the Wu-Tang legacy with hooks that pay homage to the group’s greatest hits but claim they’re more interested in paving their own way.“We’re infusing the sounds of our fathers to create a new Wu-Tang dynasty,” iNTeLL said. “Even in the first single, we made sure to add pieces of our fathers music underneath our raps so we can give the fans clues as to who our fathers were.”WU-TANG CLAN REACTS TO ‘2ND GENERATION’Although the group is founded on cementing the Wu-Tang Clan legacy, members say they have not gotten around to telling all the members of the original hip-hop group.“I’m not sure how he feels, I haveIntel-Media-SDK/MediaSDK: The Intel Media SDK
Meta-heuristic Based Clustering and Routing Algorithm for IoT-Assisted Wireless Sensor Network. Peer to Peer Networking and Application. SpringerChaurasia S, Kumar K (2023) MBASE: Meta-heuristic Based optimized location allocation algorithm for baSE station in IoT assist wireless sensor networks. Multimedia Tools and Applications, pp 1–33Senthil GA, Raaza A, Kumar N (2022) Internet of things energy efficient cluster-based routing using hybrid particle swarm optimization for wireless sensor network. Wirel Pers Commun 122.3: 2603-2619Prasanth A, Jayachitra S (2020) A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications. Peer Peer Netw Appl 13:1905–1920Article Google Scholar Vaiyapuri T, et al (2022) A novel hybrid optimization for cluster-based routing protocol in information-centric wireless sensor networks for IoT based mobile edge computing. Wirel Pers Commun 127.1: 39-62Dhiman G, Sharma R (2022) SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer. Complex Intell Syst 8(5):3779–3787Seyfollahi A, Taami T, Ghaffari A (2023) Towards developing a machine learning-metaheuristic-enhanced energy-sensitive routing framework for the internet of things. Microprocess Microsyst 96:104747Article Google Scholar Donta PK et al (2023) iCoCoA: intelligent congestion control algorithm for CoAP using deep reinforcement learning. J Ambient Intell Humaniz Comput 14(3):2951–2966Article Google Scholar Rosati R et al (2023) From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in industry 4.0. J Intell Manuf 34.1:107–121Babar M et al (2022) An optimized IoT-enabled big data analytics architecture for edge-cloud computing. IEEE Internet Things J 10(5):3995–4005Article MathSciNet Google Scholar Lv Z,. download intel widi media share download intel widi media share 1.2 intel widi media share intel widi media share 安装 出错 intel widi media share x64 intel widi media share app intel widi media share the latest intel widi media share setup intel widi media share indir intel widi media share скачатьGitHub - intel/media-driver: Intel Graphics Media
Google Scholar Jing L, Vahdani E, Tan J, Tian Y (2021) Cross-modal center loss for 3d cross-modal retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3142–3151Kan M, Shan S, Zhang H, Lao S, Chen X (2012) Multi-view discriminant analysis, pp. 188–194Kan M, Shan S, Zhang H, Lao S, Chen X (2015) Multi-view discriminant analysis. IEEE Trans Pattern Anal Mach Intell 38(1):188–194Article MATH Google Scholar Kan M, Shan S, Chen X (2016) Multi-view deep network for cross-view classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4847–4855Li G, Stalin T, Truong VT, Alvarado P (2022) Dnn-based predictive model for a batoid-inspired soft robot. IEEE Robotics Automation Lett (7–2)Li Z, Lu H, Fu H, Wang Z, Gu G (2023a) Adaptive adversarial learning based cross-modal retrieval. Eng Appl Artif Intell 123:106439. MATH Google Scholar Li Z, Lu H, Fu H, Gu G (2023b) Parallel learned generative adversarial network with multi-path subspaces for cross-modal retrieval. Inf Sci 620:84–104Article MATH Google Scholar Li M, Zhou S, Chen Y, Huang C, Jiang Y (2024) Educross: dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides. Inform Fusion 109:102428. Google Scholar Ma X, Zhang T, Xu C (2020) Multi-level correlation adversarial hashing for cross-modal retrieval. IEEE Trans Multimed. 22(12):3101–3114Article MATH Google Scholar Ma X, Wang F, Hou Y (2021) Multiple negative samples based on gan for cross-modal retrieval. In: 2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE), pp. 136–140. IEEEMirza M, Osindero S (2014) Conditional generative adversarial nets. Preprint at arXiv:1411.1784Park SM, Kim YG (2023) Visual language navigation: a survey and open challenges. Artif Intell Rev 56(1):365–427Article MATH Google Scholar Peng Y, Qi J (2019) Cm-gans: cross-modal generative adversarial networks for common representation learning. ACM Trans Multimed Comput Commun Appl (TOMM) In: 2nd International conference on technological advancements in computational science Google Scholar Bilal Z (2023) Depression detection via self-reported mental health diagnoses using BERT and LSTM. In: Memory networks information processing and management Google Scholar Qi Y (2023) Self-supervised representation learning for early detection of depression on reddit. In: Proceedings of the 2023 web conference Google Scholar Tadessi MM (2019) Detection of depression related posts in Reddit social media forum. IEEE Access Google Scholar De Melo WC (2019) Depression detection based on deep distribution learning. In: IEEE international conference on image processing (ICIP) Google Scholar Singh K (2023) Mental health monitoring using deep learning technique for Early-stage depression detection. SN Comput Sci Google Scholar Youssif A (2021) Depression detection and analysis using deep learning: study and comparative analysis. In: IEEE international conference on communication. systems and network technologies Google Scholar Kantinee K (2018) Facebook social media for depression detection in the Thai Community. In: 15th International joint conference on computer science and software engineering Google Scholar Cheng L-C (2019) Deep learning for automated sentiment analysis of social media. In: IEEE International conference on advances in social networks analysis and mining Google Scholar Yang L (2020) Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access 8 Google Scholar Rosa RL (2019) A knowledge- based recommendation system that includes sentiment analysis and deep learning. IEEE Trans Ind Inf Google Scholar Alsaeedi A (2023) A study on sentiment analysis techniques of Twitter data. Int J Adv Comput Sci Appl Google Scholar Wankhade M (2022) A survey on sentiment analysis methods, applications, and challenges. Artif Intell Rev Google Scholar Rao G (2020) A hierarchical posts representations model for identifying depressed individuals. In: Online forums. IEEE Access Google Scholar Lyua YW (2020) Exploring public attitudes of child abuse in mainland China: a sentiment analysis of China’s social media. In: Weibo. Child Youth Serv Rev Google Scholar Tariq S (2019) A novel co-training-based approach for the classification of mental illnesses using social media posts. IEEE Access Google Scholar Al Asad N (2019) Depression detection by analyzing social mediaMedia (Intel Video Processing Library, Intel Media SDK)
SA (2020) Machine learning-based approach for depression detection in Twitter using content and activity features. In: IEICE transactions informatics and systems Google Scholar Tong L (2022) Cost-sensitive boosting pruning trees for depression detection on Twitter. IEEE Trans Affect Comput Google Scholar George B (2023) Deep learning-based depression detection from social media: comparative evaluation of ML and transformer techniques. In: mdpi.com /journal/electronics Google Scholar Shen G (2017) Depression detection via harvesting social media: a multimodal dictionary learning solution. In: 26th international joint conference on artificial intelligence Google Scholar Sethi M (2020) Sentiment identification in COVID-19 specific tweets. In: International conference on electronics and sustainable communication systems Google Scholar Jose R (2016) Prediction of election result by enhanced SA on Twitter data using classifier ensemble approach. In: International conference on data mining and advanced computing Google Scholar Sharma P (2020) Experimental investigation of automated system for twitter sentiment analysis to predict the public emotions using ML algorithm. Mater Today Proc Google Scholar Gaikwad G (2016) Multiclass mood classification on twitter using lexicon dictionary and machine learning algorithms. In: International conference on inventive computation technologies Google Scholar Chun YC (2020) Multimodal depression detection on Instagram considering time interval of posts. J Intell Inform Syst Google Scholar Andrew G (2017) Reece: Instagram photos reveal predictive markers of depression. EPJ Data Sci J Google Scholar Sharma K., Singh V (2019) Depression detection using deep learning in social media posts. Proc Comput Sci Google Scholar Rafiqul IM (2019) Depression detection from social network data using machine learning techniques. J Affect Disorders Google Scholar Katchapakirin (2018) Facebook social media for depression detection. In: Thai community in computers in human behavior Google Scholar Gkotsis (2021) An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms. Science Direct Google Scholar Alshammari MA (2020) Depression detection from facebook posts using deep learning. IEEE Access J Google Scholar Arman C (2017) Detecting depression risk from text data using word embeddings and machine learning. In: Proceedings of the conference on empirical methods Google Scholar Chen Z (2023) Detecting reddit users with depression using a hybrid neural network.Intel-Media-SDK/MediaSDK: The Intel Media SDK - GitHub
ReferencesFu, Y., Guo, G., Huang, T.S.: Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 1955–1976 (2010)Article Google Scholar Suo, J., Zhu, S.C., Shan, S., Chen, X.: A compositional and dynamic model for face aging. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 385–401 (2009) Google Scholar Hill, C.M., Solomon, C.J., Gibson, S.J.: Aging the human face-a statistically rigorous approach (2005) Google Scholar Wang, W., et al.: Recurrent face aging. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2378–2386 (2016) Google Scholar Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002)Article Google Scholar Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014) Google Scholar Shu, X., Tang, J., Lai, H., Liu, L., Yan, S.: Personalized age progression with aging dictionary. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3970–3978 (2015) Google Scholar Kokate, P., Joshi, A.D., Tamizharasan, P.S.: An empirical comparison of generative adversarial network (GAN) measures. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds.) Advances in Communication and Computational Technology. LNEE, vol. 668, pp. 1383–1396. Springer, Singapore (2021). Google Scholar Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXivpreprint arXiv:1411.1784 (2014)Cheng, Z., et al.: Deep convolutional autoencoder-based lossy image compression. In: 2018 Picture Coding Symposium (PCS). IEEE (2018) Google Scholar Patterson, E., Sethuram, A., Albert, M., Ricanek, K., King, M.: Aspects of age variation in facial morphology affecting biometrics. In: 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE (2007) Google Scholar Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017) Google Scholar Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017) Google Scholar Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial. download intel widi media share download intel widi media share 1.2 intel widi media share intel widi media share 安装 出错 intel widi media share x64 intel widi media share app intel widi media share the latest intel widi media share setup intel widi media share indir intel widi media share скачатьIntel Media SDK Intel Media Server Studio Historical
15(1):1–24Article MATH Google Scholar Peng Y, Zhai X, Zhao Y, Huang X (2015) Semi-supervised cross-media feature learning with unified patch graph regularization. IEEE Trans Circuits Syst Video Technol 26(3):583–596Article MATH Google Scholar Peng Y, Huang X, Qi J (2016) Cross-media shared representation by hierarchical learning with multiple deep networks. In: IJCAI, pp. 3846–3853Peng Y, Qi J, Huang X, Yuan Y (2017a) Ccl: cross-modal correlation learning with multigrained fusion by hierarchical network. IEEE Trans Multimed 20(2):405–420Article MATH Google Scholar Peng Y, Huang X, Zhao Y (2017b) An overview of cross-media retrieval: concepts, methodologies, benchmarks, and challenges. IEEE Trans Circuits Syst Video Technol 28(9):2372–2385Article MATH Google Scholar Peng Y, Qi J, Yuan Y (2018) Modality-specific cross-modal similarity measurement with recurrent attention network. IEEE Trans Image Process 27(11):5585–5599Article MathSciNet MATH Google Scholar Peng Y, Ye Z, Qi J, Zhuo Y (2020) Unsupervised visual-textual correlation learning with fine-grained semantic alignment. IEEE Transactions on CyberneticsPerez-Martin J, Bustos B, Guimarães SJF, Sipiran I, Pérez J, Said GC (2022) A comprehensive review of the video-to-text problem. Artif Intell Rev, 1–75Rashtchian C, Young P, Hodosh M, Hockenmaier J (2010) Collecting image annotations using amazon’s mechanical turk. In: Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, pp. 139–147Rasiwasia N, Costa Pereira J, Coviello E, Doyle G, Lanckriet GR, Levy R, Vasconcelos N (2010) A new approach to cross-modal multimedia retrieval. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 251–260Rupnik J, Shawe-Taylor J (2010) Multi-view canonical correlation analysis. In: Conference on Data Mining and Data Warehouses (SiKDD 2010), pp. 1–4Shi Y, Zhao Y, Liu X, Zheng F, Ou W, You X, Peng Q (2022) Deep adaptively-enhanced hashing with discriminative similarity guidance for unsupervised cross-modal retrieval. IEEE Transactions on Circuits and Systems for Video TechnologyShu Z, Bai Y, ZhangComments
Yet to speak with him about that,” iNTeLL said when asked if U-God, his father, approves of his latest venture. “He’s supportive of everything I do but with the ‘2nd Generation Wu’ stuff because it’s only been a few days since the single drop, I’m not sure if he’s heard it yet.”PXWER, son of Method Man played the track for his father who has been “in his corner” through this venture.“He’s supportive and he’s just letting me do what I do," PXWER said. ““I take serious pride in being a child of Wu-Tang but we’re also trying to cement ourselves in our careers.”“2nd Generation Wu” members say they will release a music video to their debut single by the end of 2019 and plan to drop an album in the “near future." “7 O.D,” produced by Jo-Jo Pellegrino and J.Glaze, racked up 29,000 views in three days.“We comin’ in strong,” iNTeLL said. “This isn’t a ‘dip our toe in’ kind of thing. This plan is in full force, we here.”If you purchase a product or register for an account through a link on our site, we may receive compensation. By using this site, you consent to our User Agreement and agree that your clicks, interactions, and personal information may be collected, recorded, and/or stored by us and social media and other third-party partners in accordance with our Privacy Policy.
2025-04-10"Young Dirty Bastard," iNTeLL and PXWER are the children of "Old Dirty Bastard," U-God and Method Man, who founded the legendary hip-hop group Wu-Tang Clan. (Photo by Lebanese Ether)By Victoria Priola, eCommerce writerSTATEN ISLAND, N.Y.-- Wu-Tang Clan is for the children. Even their children know that.Sons of the legendary hip-hop group’s founding members came together to create “2nd Generation Wu,” a music group consisting of iNTeLL, the son of U-God; PXWER, son of Method Man; SUN GOD, son of Ghostface Killah and “Young Dirty Bastard,” son of the late “Old Dirty Bastard.”“Growing up, I’ve always had the mindset of 'I am who I am, my father is who he is,” iNTeLL told SILive.com. “We’re all next in line to receive ‘Wu-Tang is forever’ but at the same time, we all have our own unique sound and we’re ready to share that together.”The group dropped its debut single this month titled “7 O.D.” through Dock Street Records, based inside Amendment 18 in Stapleton. The opening lyric to the debut single is “God made everything around me; Forget about the money; None of it is real y’all.” (The video below has some choice language that may not be suitable for audiences of all ages. Viewers digression is advised.) “2nd Generation Wu" dropped their single around the 26th anniversary of Wu-Tang Clan’s praised album “Enter the Wu-Tang.”A HIP-HOP FAMILY TREESince the early 90′s when members of Wu-Tang Clan found themselves rapping in the streets of Shaolin, a familial connection was created and passed on to the lives of their children.------------------------------------------------------------------------Watch “Wu-Tang: An American Saga” on Hulu------------------------------------------------------------------------iNTeLL and PXWER are blood related through their mothers. The pair spent their lives pursuing music through Staten Island events and New York City venues. Over the course of 10 years, connections to the offspring of the other Wu-Tang Clan members began to form and the group -- 2nd Generation Wu" -- officially made their debut at a local hip-hop show on the stage of Amendment 18 in July.“Once I was aware that we all do music, I was like ‘holy s***,'” iNTeLL said. “We aren’t really in communication like that but we all have music in our DNA. Now imagine if we all get together. How powerful that would be.”The group wants to make one thing clear: They are not a cover band of their fathers. “2nd Generation” members honor the Wu-Tang legacy with hooks that pay homage to the group’s greatest hits but claim they’re more interested in paving their own way.“We’re infusing the sounds of our fathers to create a new Wu-Tang dynasty,” iNTeLL said. “Even in the first single, we made sure to add pieces of our fathers music underneath our raps so we can give the fans clues as to who our fathers were.”WU-TANG CLAN REACTS TO ‘2ND GENERATION’Although the group is founded on cementing the Wu-Tang Clan legacy, members say they have not gotten around to telling all the members of the original hip-hop group.“I’m not sure how he feels, I have
2025-04-23Meta-heuristic Based Clustering and Routing Algorithm for IoT-Assisted Wireless Sensor Network. Peer to Peer Networking and Application. SpringerChaurasia S, Kumar K (2023) MBASE: Meta-heuristic Based optimized location allocation algorithm for baSE station in IoT assist wireless sensor networks. Multimedia Tools and Applications, pp 1–33Senthil GA, Raaza A, Kumar N (2022) Internet of things energy efficient cluster-based routing using hybrid particle swarm optimization for wireless sensor network. Wirel Pers Commun 122.3: 2603-2619Prasanth A, Jayachitra S (2020) A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications. Peer Peer Netw Appl 13:1905–1920Article Google Scholar Vaiyapuri T, et al (2022) A novel hybrid optimization for cluster-based routing protocol in information-centric wireless sensor networks for IoT based mobile edge computing. Wirel Pers Commun 127.1: 39-62Dhiman G, Sharma R (2022) SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer. Complex Intell Syst 8(5):3779–3787Seyfollahi A, Taami T, Ghaffari A (2023) Towards developing a machine learning-metaheuristic-enhanced energy-sensitive routing framework for the internet of things. Microprocess Microsyst 96:104747Article Google Scholar Donta PK et al (2023) iCoCoA: intelligent congestion control algorithm for CoAP using deep reinforcement learning. J Ambient Intell Humaniz Comput 14(3):2951–2966Article Google Scholar Rosati R et al (2023) From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in industry 4.0. J Intell Manuf 34.1:107–121Babar M et al (2022) An optimized IoT-enabled big data analytics architecture for edge-cloud computing. IEEE Internet Things J 10(5):3995–4005Article MathSciNet Google Scholar Lv Z,
2025-04-04Google Scholar Jing L, Vahdani E, Tan J, Tian Y (2021) Cross-modal center loss for 3d cross-modal retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3142–3151Kan M, Shan S, Zhang H, Lao S, Chen X (2012) Multi-view discriminant analysis, pp. 188–194Kan M, Shan S, Zhang H, Lao S, Chen X (2015) Multi-view discriminant analysis. IEEE Trans Pattern Anal Mach Intell 38(1):188–194Article MATH Google Scholar Kan M, Shan S, Chen X (2016) Multi-view deep network for cross-view classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4847–4855Li G, Stalin T, Truong VT, Alvarado P (2022) Dnn-based predictive model for a batoid-inspired soft robot. IEEE Robotics Automation Lett (7–2)Li Z, Lu H, Fu H, Wang Z, Gu G (2023a) Adaptive adversarial learning based cross-modal retrieval. Eng Appl Artif Intell 123:106439. MATH Google Scholar Li Z, Lu H, Fu H, Gu G (2023b) Parallel learned generative adversarial network with multi-path subspaces for cross-modal retrieval. Inf Sci 620:84–104Article MATH Google Scholar Li M, Zhou S, Chen Y, Huang C, Jiang Y (2024) Educross: dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides. Inform Fusion 109:102428. Google Scholar Ma X, Zhang T, Xu C (2020) Multi-level correlation adversarial hashing for cross-modal retrieval. IEEE Trans Multimed. 22(12):3101–3114Article MATH Google Scholar Ma X, Wang F, Hou Y (2021) Multiple negative samples based on gan for cross-modal retrieval. In: 2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE), pp. 136–140. IEEEMirza M, Osindero S (2014) Conditional generative adversarial nets. Preprint at arXiv:1411.1784Park SM, Kim YG (2023) Visual language navigation: a survey and open challenges. Artif Intell Rev 56(1):365–427Article MATH Google Scholar Peng Y, Qi J (2019) Cm-gans: cross-modal generative adversarial networks for common representation learning. ACM Trans Multimed Comput Commun Appl (TOMM)
2025-04-09