Literatura científica selecionada sobre o tema "Visual and semantic embedding"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Índice
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Visual and semantic embedding".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Visual and semantic embedding"
Zhang, Yuanpeng, Jingye Guan, Haobo Wang, Kaiming Li, Ying Luo e Qun Zhang. "Generalized Zero-Shot Space Target Recognition Based on Global-Local Visual Feature Embedding Network". Remote Sensing 15, n.º 21 (28 de outubro de 2023): 5156. http://dx.doi.org/10.3390/rs15215156.
Texto completo da fonteYeh, Mei-Chen, e Yi-Nan Li. "Multilabel Deep Visual-Semantic Embedding". IEEE Transactions on Pattern Analysis and Machine Intelligence 42, n.º 6 (1 de junho de 2020): 1530–36. http://dx.doi.org/10.1109/tpami.2019.2911065.
Texto completo da fonteMerkx, Danny, e Stefan L. Frank. "Learning semantic sentence representations from visually grounded language without lexical knowledge". Natural Language Engineering 25, n.º 4 (julho de 2019): 451–66. http://dx.doi.org/10.1017/s1351324919000196.
Texto completo da fonteZhou, Mo, Zhenxing Niu, Le Wang, Zhanning Gao, Qilin Zhang e Gang Hua. "Ladder Loss for Coherent Visual-Semantic Embedding". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 13050–57. http://dx.doi.org/10.1609/aaai.v34i07.7006.
Texto completo da fonteGe, Jiannan, Hongtao Xie, Shaobo Min e Yongdong Zhang. "Semantic-guided Reinforced Region Embedding for Generalized Zero-Shot Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 2 (18 de maio de 2021): 1406–14. http://dx.doi.org/10.1609/aaai.v35i2.16230.
Texto completo da fonteNguyen, Huy Manh, Tomo Miyazaki, Yoshihiro Sugaya e Shinichiro Omachi. "Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence". Applied Sciences 11, n.º 7 (3 de abril de 2021): 3214. http://dx.doi.org/10.3390/app11073214.
Texto completo da fonteMATSUBARA, Takashi. "Target-Oriented Deformation of Visual-Semantic Embedding Space". IEICE Transactions on Information and Systems E104.D, n.º 1 (1 de janeiro de 2021): 24–33. http://dx.doi.org/10.1587/transinf.2020mup0003.
Texto completo da fonteTang, Qi, Yao Zhao, Meiqin Liu, Jian Jin e Chao Yao. "Semantic Lens: Instance-Centric Semantic Alignment for Video Super-resolution". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 6 (24 de março de 2024): 5154–61. http://dx.doi.org/10.1609/aaai.v38i6.28321.
Texto completo da fonteKeller, Patrick, Abdoul Kader Kaboré, Laura Plein, Jacques Klein, Yves Le Traon e Tegawendé F. Bissyandé. "What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning". ACM Transactions on Software Engineering and Methodology 31, n.º 2 (30 de abril de 2022): 1–34. http://dx.doi.org/10.1145/3485135.
Texto completo da fonteHe, Hai, e Haibo Yang. "Deep Visual Semantic Embedding with Text Data Augmentation and Word Embedding Initialization". Mathematical Problems in Engineering 2021 (28 de maio de 2021): 1–8. http://dx.doi.org/10.1155/2021/6654071.
Texto completo da fonteTeses / dissertações sobre o assunto "Visual and semantic embedding"
Engilberge, Martin. "Deep Inside Visual-Semantic Embeddings". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS150.
Texto completo da fonteNowadays Artificial Intelligence (AI) is omnipresent in our society. The recentdevelopment of learning methods based on deep neural networks alsocalled "Deep Learning" has led to a significant improvement in visual representation models.and textual.In this thesis, we aim to further advance image representation and understanding.Revolving around Visual Semantic Embedding (VSE) approaches, we explore different directions: We present relevant background covering images and textual representation and existing multimodal approaches. We propose novel architectures further improving retrieval capability of VSE and we extend VSE models to novel applications and leverage embedding models to visually ground semantic concept. Finally, we delve into the learning process andin particular the loss function by learning differentiable approximation of ranking based metric
Wang, Qian. "Zero-shot visual recognition via latent embedding learning". Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/zeroshot-visual-recognition-via-latent-embedding-learning(bec510af-6a53-4114-9407-75212e1a08e1).html.
Texto completo da fonteFicapal, Vila Joan. "Anemone: a Visual Semantic Graph". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252810.
Texto completo da fonteSemantiska grafer har använts för att optimera olika processer för naturlig språkbehandling samt för att förbättra sökoch informationsinhämtningsuppgifter. I de flesta fall har sådana semantiska grafer konstruerats genom övervakade maskininlärningsmetoder som förutsätter manuellt kurerade ontologier såsom Wikipedia eller liknande. I denna uppsats, som består av två delar, undersöker vi i första delen möjligheten att automatiskt generera en semantisk graf från ett ad hoc dataset bestående av 50 000 tidningsartiklar på ett helt oövervakat sätt. Användbarheten hos den visuella representationen av den resulterande grafen testas på 14 försökspersoner som utför grundläggande informationshämtningsuppgifter på en delmängd av artiklarna. Vår studie visar att vår funktionalitet är lönsam för att hitta och dokumentera likhet med varandra, och den visuella kartan som produceras av vår artefakt är visuellt användbar. I den andra delen utforskar vi möjligheten att identifiera entitetsrelationer på ett oövervakat sätt genom att använda abstraktiva djupa inlärningsmetoder för meningsomformulering. De omformulerade meningarna utvärderas kvalitativt med avseende på grammatisk korrekthet och meningsfullhet såsom detta uppfattas av 14 testpersoner. Vi utvärderar negativt resultaten av denna andra del, eftersom de inte har varit tillräckligt bra för att få någon definitiv slutsats, men har istället öppnat nya dörrar för att utforska.
Jakeš, Jan. "Visipedia - Embedding-driven Visual Feature Extraction and Learning". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236120.
Texto completo da fonteGao, Jizhou. "VISUAL SEMANTIC SEGMENTATION AND ITS APPLICATIONS". UKnowledge, 2013. http://uknowledge.uky.edu/cs_etds/14.
Texto completo da fonteLiu, Jingen. "Learning Semantic Features for Visual Recognition". Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3358.
Texto completo da fontePh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD
Nguyen, Duc Minh Chau. "Affordance learning for visual-semantic perception". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2443.
Texto completo da fonteChen, Yifu. "Deep learning for visual semantic segmentation". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS200.
Texto completo da fonteIn this thesis, we are interested in Visual Semantic Segmentation, one of the high-level task that paves the way towards complete scene understanding. Specifically, it requires a semantic understanding at the pixel level. With the success of deep learning in recent years, semantic segmentation problems are being tackled using deep architectures. In the first part, we focus on the construction of a more appropriate loss function for semantic segmentation. More precisely, we define a novel loss function by employing a semantic edge detection network. This loss imposes pixel-level predictions to be consistent with the ground truth semantic edge information, and thus leads to better shaped segmentation results. In the second part, we address another important issue, namely, alleviating the need for training segmentation models with large amounts of fully annotated data. We propose a novel attribution method that identifies the most significant regions in an image considered by classification networks. We then integrate our attribution method into a weakly supervised segmentation framework. The semantic segmentation models can thus be trained with only image-level labeled data, which can be easily collected in large quantities. All models proposed in this thesis are thoroughly experimentally evaluated on multiple datasets and the results are competitive with the literature
Fan, Wei. "Image super-resolution using neighbor embedding over visual primitive manifolds /". View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20FAN.
Texto completo da fonteHanwell, David. "Weakly supervised learning of visual semantic attributes". Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687063.
Texto completo da fonteLivros sobre o assunto "Visual and semantic embedding"
Endert, Alex. Semantic Interaction for Visual Analytics. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02603-4.
Texto completo da fontePaquette, Gilbert. Visual knowledge modeling for semantic web technologies: Models and ontologies. Hershey, PA: Information Science Reference, 2010.
Encontre o texto completo da fonteHussam, Ali. Semantic highlighting: An approach to communicating information and knowledge through visual metadata. [s.l: The Author], 1999.
Encontre o texto completo da fonteValkola, Jarmo. Perceiving the visual in cinema: Semantic approaches to film form and meaning. Jyväskylä: Jyväskylän Yliopisto, 1993.
Encontre o texto completo da fonteChen, Chaomei. Effects of spatial-semantic interfaces in visual information retrieval: Three experimental studies. [Great Britain]: Resource, 2002.
Encontre o texto completo da fonteK, kokula Krishna Hari, ed. Multi-secret Semantic Visual Cryptographic Protocol for Securing Image Communications: ICCS 2014. Bangkok, Thailand: Association of Scientists, Developers and Faculties, 2014.
Encontre o texto completo da fonteBratko, Aleksandr. Artificial intelligence, legal system and state functions. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064996.
Texto completo da fonteVideo segmentation and its applications. New York: Springer, 2011.
Encontre o texto completo da fonteStoenescu, Livia. The Pictorial Art of El Greco. NL Amsterdam: Amsterdam University Press, 2019. http://dx.doi.org/10.5117/9789462989009.
Texto completo da fonteZhang, Yu-jin. Semantic-Based Visual Information Retrieval. IRM Press, 2006.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Visual and semantic embedding"
Wang, Haoran, Ying Zhang, Zhong Ji, Yanwei Pang e Lin Ma. "Consensus-Aware Visual-Semantic Embedding for Image-Text Matching". In Computer Vision – ECCV 2020, 18–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58586-0_2.
Texto completo da fonteYang, Zhanbo, Li Li, Jun He, Zixi Wei, Li Liu e Jun Liao. "Multimodal Learning with Triplet Ranking Loss for Visual Semantic Embedding Learning". In Knowledge Science, Engineering and Management, 763–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29551-6_67.
Texto completo da fonteJiang, Zhukai, e Zhichao Lian. "Self-supervised Visual-Semantic Embedding Network Based on Local Label Optimization". In Machine Learning for Cyber Security, 400–412. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20102-8_31.
Texto completo da fonteFilntisis, Panagiotis Paraskevas, Niki Efthymiou, Gerasimos Potamianos e Petros Maragos. "Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss". In Computer Vision – ECCV 2020 Workshops, 747–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66415-2_52.
Texto completo da fonteValério, Rodrigo, e João Magalhães. "Learning Semantic-Visual Embeddings with a Priority Queue". In Pattern Recognition and Image Analysis, 67–81. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36616-1_6.
Texto completo da fonteSyed, Arsal, e Brendan Tran Morris. "CNN, Segmentation or Semantic Embeddings: Evaluating Scene Context for Trajectory Prediction". In Advances in Visual Computing, 706–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64559-5_56.
Texto completo da fonteSchall, Konstantin, Nico Hezel, Klaus Jung e Kai Uwe Barthel. "Vibro: Video Browsing with Semantic and Visual Image Embeddings". In MultiMedia Modeling, 665–70. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27077-2_56.
Texto completo da fonteChen, Yanbei, e Loris Bazzani. "Learning Joint Visual Semantic Matching Embeddings for Language-Guided Retrieval". In Computer Vision – ECCV 2020, 136–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58542-6_9.
Texto completo da fonteTheodoridou, Christina, Andreas Kargakos, Ioannis Kostavelis, Dimitrios Giakoumis e Dimitrios Tzovaras. "Spatially-Constrained Semantic Segmentation with Topological Maps and Visual Embeddings". In Lecture Notes in Computer Science, 117–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87156-7_10.
Texto completo da fonteThoma, Steffen, Achim Rettinger e Fabian Both. "Towards Holistic Concept Representations: Embedding Relational Knowledge, Visual Attributes, and Distributional Word Semantics". In Lecture Notes in Computer Science, 694–710. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68288-4_41.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Visual and semantic embedding"
Li, Zheng, Caili Guo, Zerun Feng, Jenq-Neng Hwang e Xijun Xue. "Multi-View Visual Semantic Embedding". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/158.
Texto completo da fonteRen, Zhou, Hailin Jin, Zhe Lin, Chen Fang e Alan Yuille. "Multiple Instance Visual-Semantic Embedding". In British Machine Vision Conference 2017. British Machine Vision Association, 2017. http://dx.doi.org/10.5244/c.31.89.
Texto completo da fonteWehrmann, Jônatas, e Rodrigo C. Barros. "Language-Agnostic Visual-Semantic Embeddings". In Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/ctd.2021.15751.
Texto completo da fonteLi, Binglin, e Yang Wang. "Visual Relationship Detection Using Joint Visual-Semantic Embedding". In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8546097.
Texto completo da fonteJi, Rongrong, Hongxun Yao, Xiaoshuai Sun, Bineng Zhong e Wen Gao. "Towards semantic embedding in visual vocabulary". In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5540118.
Texto completo da fonteHong, Ziming, Shiming Chen, Guo-Sen Xie, Wenhan Yang, Jian Zhao, Yuanjie Shao, Qinmu Peng e Xinge You. "Semantic Compression Embedding for Generative Zero-Shot Learning". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/134.
Texto completo da fontePerez-Martin, Jesus, Jorge Perez e Benjamin Bustos. "Visual-Syntactic Embedding for Video Captioning". In LatinX in AI at Computer Vision and Pattern Recognition Conference 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai202106259.
Texto completo da fonteZeng, Zhixian, Jianjun Cao, Nianfeng Weng, Guoquan Jiang, Yizhuo Rao e Yuxin Xu. "Softmax Pooling for Super Visual Semantic Embedding". In 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2021. http://dx.doi.org/10.1109/iemcon53756.2021.9623131.
Texto completo da fonteZhang, Licheng, Xianzhi Wang, Lina Yao, Lin Wu e Feng Zheng. "Zero-Shot Object Detection via Learning an Embedding from Semantic Space to Visual Space". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/126.
Texto completo da fonteSong, Yale, e Mohammad Soleymani. "Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval". In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00208.
Texto completo da fonteRelatórios de organizações sobre o assunto "Visual and semantic embedding"
Kud, A. A. Figures and Tables. Reprinted from “Comprehensive сlassification of virtual assets”, A. A. Kud, 2021, International Journal of Education and Science, 4(1), 52–75. KRPOCH, 2021. http://dx.doi.org/10.26697/reprint.ijes.2021.1.6.a.kud.
Texto completo da fonteTabinskyy, Yaroslav. VISUAL CONCEPTS OF PHOTO IN THE MEDIA (ON THE EXAMPLE OF «UKRAINER» AND «REPORTERS»). Ivan Franko National University of Lviv, março de 2021. http://dx.doi.org/10.30970/vjo.2021.50.11099.
Texto completo da fonteMbani, Benson, Timm Schoening e Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, maio de 2023. http://dx.doi.org/10.3289/sw_2_2023.
Texto completo da fonteYatsymirska, Mariya. SOCIAL EXPRESSION IN MULTIMEDIA TEXTS. Ivan Franko National University of Lviv, fevereiro de 2021. http://dx.doi.org/10.30970/vjo.2021.49.11072.
Texto completo da fonte