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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Marius Leordeanu | - |
dc.date.accessioned | 2024-12-18T07:02:34Z | - |
dc.date.available | 2024-12-18T07:02:34Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://thuvienso.thanglong.edu.vn//handle/TLU/11812 | - |
dc.description.abstract | This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. | vi |
dc.language.iso | en | vi |
dc.publisher | Springer | vi |
dc.subject | Computer Vision | Neural networks | Graph-based techniques | Mạng nơron | Kỹ thuật dựa trên đồ thị | vi |
dc.title | Unsupervised learning in space and time : a modern approach for computer vision using graph-based techniques and deep neural networks | vi |
dc.type | Sách/Book | vi |
Appears in Collections | Tin học |
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