Gotowa bibliografia na temat „Deep Video Representations”
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Artykuły w czasopismach na temat "Deep Video Representations"
Feichtenhofer, Christoph, Axel Pinz, Richard P. Wildes i Andrew Zisserman. "Deep Insights into Convolutional Networks for Video Recognition". International Journal of Computer Vision 128, nr 2 (29.10.2019): 420–37. http://dx.doi.org/10.1007/s11263-019-01225-w.
Pełny tekst źródłaPandeya, Yagya Raj, Bhuwan Bhattarai i Joonwhoan Lee. "Deep-Learning-Based Multimodal Emotion Classification for Music Videos". Sensors 21, nr 14 (20.07.2021): 4927. http://dx.doi.org/10.3390/s21144927.
Pełny tekst źródłaLjubešić, Nikola. "‟Deep lexicography” – Fad or Opportunity?" Rasprave Instituta za hrvatski jezik i jezikoslovlje 46, nr 2 (30.10.2020): 839–52. http://dx.doi.org/10.31724/rihjj.46.2.21.
Pełny tekst źródłaKumar, Vidit, Vikas Tripathi i Bhaskar Pant. "Learning Unsupervised Visual Representations using 3D Convolutional Autoencoder with Temporal Contrastive Modeling for Video Retrieval". International Journal of Mathematical, Engineering and Management Sciences 7, nr 2 (14.03.2022): 272–87. http://dx.doi.org/10.33889/ijmems.2022.7.2.018.
Pełny tekst źródłaVihlman, Mikko, i Arto Visala. "Optical Flow in Deep Visual Tracking". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 07 (3.04.2020): 12112–19. http://dx.doi.org/10.1609/aaai.v34i07.6890.
Pełny tekst źródłaRouast, Philipp V., i Marc T. P. Adam. "Learning Deep Representations for Video-Based Intake Gesture Detection". IEEE Journal of Biomedical and Health Informatics 24, nr 6 (czerwiec 2020): 1727–37. http://dx.doi.org/10.1109/jbhi.2019.2942845.
Pełny tekst źródłaLi, Jialu, Aishwarya Padmakumar, Gaurav Sukhatme i Mohit Bansal. "VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 17 (24.03.2024): 18517–26. http://dx.doi.org/10.1609/aaai.v38i17.29813.
Pełny tekst źródłaHu, Yueyue, Shiliang Sun, Xin Xu i Jing Zhao. "Multi-View Deep Attention Network for Reinforcement Learning (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 10 (3.04.2020): 13811–12. http://dx.doi.org/10.1609/aaai.v34i10.7177.
Pełny tekst źródłaDong, Zhen, Chenchen Jing, Mingtao Pei i Yunde Jia. "Deep CNN based binary hash video representations for face retrieval". Pattern Recognition 81 (wrzesień 2018): 357–69. http://dx.doi.org/10.1016/j.patcog.2018.04.014.
Pełny tekst źródłaPsallidas, Theodoros, i Evaggelos Spyrou. "Video Summarization Based on Feature Fusion and Data Augmentation". Computers 12, nr 9 (15.09.2023): 186. http://dx.doi.org/10.3390/computers12090186.
Pełny tekst źródłaRozprawy doktorskie na temat "Deep Video Representations"
Yang, Yang. "Learning Hierarchical Representations for Video Analysis Using Deep Learning". Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5892.
Pełny tekst źródłaPh.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering
Sudhakaran, Swathikiran. "Deep Neural Architectures for Video Representation Learning". Doctoral thesis, Università degli studi di Trento, 2019. https://hdl.handle.net/11572/369191.
Pełny tekst źródłaSudhakaran, Swathikiran. "Deep Neural Architectures for Video Representation Learning". Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3731/1/swathi_thesis_rev1.pdf.
Pełny tekst źródłaSun, Shuyang. "Designing Motion Representation in Videos". Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/19724.
Pełny tekst źródłaMazari, Ahmed. "Apprentissage profond pour la reconnaissance d’actions en vidéos". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS171.
Pełny tekst źródłaNowadays, video contents are ubiquitous through the popular use of internet and smartphones, as well as social media. Many daily life applications such as video surveillance and video captioning, as well as scene understanding require sophisticated technologies to process video data. It becomes of crucial importance to develop automatic means to analyze and to interpret the large amount of available video data. In this thesis, we are interested in video action recognition, i.e. the problem of assigning action categories to sequences of videos. This can be seen as a key ingredient to build the next generation of vision systems. It is tackled with AI frameworks, mainly with ML and Deep ConvNets. Current ConvNets are increasingly deeper, data-hungrier and this makes their success tributary of the abundance of labeled training data. ConvNets also rely on (max or average) pooling which reduces dimensionality of output layers (and hence attenuates their sensitivity to the availability of labeled data); however, this process may dilute the information of upstream convolutional layers and thereby affect the discrimination power of the trained video representations, especially when the learned action categories are fine-grained
"Video2Vec: Learning Semantic Spatio-Temporal Embedding for Video Representations". Master's thesis, 2016. http://hdl.handle.net/2286/R.I.40765.
Pełny tekst źródłaDissertation/Thesis
Masters Thesis Computer Science 2016
(7486115), Gagandeep Singh Khanuja. "A STUDY OF REAL TIME SEARCH IN FLOOD SCENES FROM UAV VIDEOS USING DEEP LEARNING TECHNIQUES". Thesis, 2019.
Znajdź pełny tekst źródłaSouček, Tomáš. "Detekce střihů a vyhledávání známých scén ve videu s pomocí metod hlubokého učení". Master's thesis, 2020. http://www.nusl.cz/ntk/nusl-434967.
Pełny tekst źródłaKsiążki na temat "Deep Video Representations"
Aguayo, Angela J. Documentary Resistance. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190676216.001.0001.
Pełny tekst źródłaAnderson, Crystal S. Soul in Seoul. University Press of Mississippi, 2020. http://dx.doi.org/10.14325/mississippi/9781496830098.001.0001.
Pełny tekst źródłaCzęści książek na temat "Deep Video Representations"
Loban, Rhett. "Designing to produce deep representations". W Embedding Culture into Video Games and Game Design, 140–52. London: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003276289-10.
Pełny tekst źródłaYao, Yuan, Zhiyuan Liu, Yankai Lin i Maosong Sun. "Cross-Modal Representation Learning". W Representation Learning for Natural Language Processing, 211–40. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1600-9_7.
Pełny tekst źródłaMao, Feng, Xiang Wu, Hui Xue i Rong Zhang. "Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network". W Lecture Notes in Computer Science, 262–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11018-5_24.
Pełny tekst źródłaBecerra-Riera, Fabiola, Annette Morales-González i Heydi Méndez-Vázquez. "Exploring Local Deep Representations for Facial Gender Classification in Videos". W Progress in Artificial Intelligence and Pattern Recognition, 104–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01132-1_12.
Pełny tekst źródłaZhao, Kemeng, Liangrui Peng, Ning Ding, Gang Yao, Pei Tang i Shengjin Wang. "Deep Representation Learning for License Plate Recognition in Low Quality Video Images". W Advances in Visual Computing, 202–14. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47966-3_16.
Pełny tekst źródłaChen, Yixiong, Chunhui Zhang, Li Liu, Cheng Feng, Changfeng Dong, Yongfang Luo i Xiang Wan. "USCL: Pretraining Deep Ultrasound Image Diagnosis Model Through Video Contrastive Representation Learning". W Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 627–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_60.
Pełny tekst źródłaDhurgadevi, M., D. Vimal Kumar, R. Senthilkumar i K. Gunasekaran. "Detection of Video Anomaly in Public With Deep Learning Algorithm". W Advances in Psychology, Mental Health, and Behavioral Studies, 81–95. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-4143-8.ch004.
Pełny tekst źródłaAsma, Stephen T. "Drama In The Diorama: The Confederation & Art and Science". W Stuffed Animals & pickled Heads, 240–88. Oxford University PressNew York, NY, 2001. http://dx.doi.org/10.1093/oso/9780195130508.003.0007.
Pełny tekst źródłaVerma, Gyanendra K. "Emotions Modelling in 3D Space". W Multimodal Affective Computing: Affective Information Representation, Modelling, and Analysis, 128–47. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124453123010013.
Pełny tekst źródłaNandal, Priyanka. "Motion Imitation for Monocular Videos". W Examining the Impact of Deep Learning and IoT on Multi-Industry Applications, 118–35. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7511-6.ch008.
Pełny tekst źródłaStreszczenia konferencji na temat "Deep Video Representations"
Morere, Olivier, Hanlin Goh, Antoine Veillard, Vijay Chandrasekhar i Jie Lin. "Co-regularized deep representations for video summarization". W 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351387.
Pełny tekst źródłaYu, Feiwu, Xinxiao Wu, Yuchao Sun i Lixin Duan. "Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks". W Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/154.
Pełny tekst źródłaPernici, Federico, Federico Bartoli, Matteo Bruni i Alberto Del Bimbo. "Memory Based Online Learning of Deep Representations from Video Streams". W 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00247.
Pełny tekst źródłaJung, Ilchae, Minji Kim, Eunhyeok Park i Bohyung Han. "Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking". W 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.2022/140.
Pełny tekst źródłaGarcia-Gonzalez, Jorge, Rafael M. Luque-Baena, Juan M. Ortiz-de-Lazcano-Lobato i Ezequiel Lopez-Rubio. "Moving Object Detection in Noisy Video Sequences Using Deep Convolutional Disentangled Representations". W 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897305.
Pełny tekst źródłaParchami, Mostafa, Saman Bashbaghi, Eric Granger i Saif Sayed. "Using deep autoencoders to learn robust domain-invariant representations for still-to-video face recognition". W 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2017. http://dx.doi.org/10.1109/avss.2017.8078553.
Pełny tekst źródłaBueno-Benito, Elena, Biel Tura i Mariella Dimiccoli. "Leveraging Triplet Loss for Unsupervised Action Segmentation". W LatinX in AI at Computer Vision and Pattern Recognition Conference 2023. Journal of LatinX in AI Research, 2023. http://dx.doi.org/10.52591/lxai202306185.
Pełny tekst źródłaKich, Victor Augusto, Junior Costa de Jesus, Ricardo Bedin Grando, Alisson Henrique Kolling, Gabriel Vinícius Heisler i Rodrigo da Silva Guerra. "Deep Reinforcement Learning Using a Low-Dimensional Observation Filter for Visual Complex Video Game Playing". W Anais Estendidos do Simpósio Brasileiro de Games e Entretenimento Digital. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbgames_estendido.2021.19659.
Pełny tekst źródłaFan, Tingyu, Linyao Gao, Yiling Xu, Zhu Li i Dong Wang. "D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction". W 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.2022/126.
Pełny tekst źródłaLi, Yang, Kan Li i Xinxin Wang. "Deeply-Supervised CNN Model for Action Recognition with Trainable Feature Aggregation". W Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/112.
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