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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Yadang, Chen | - |
| dc.contributor.author | Duolin, Wang | - |
| dc.contributor.author | Zhiguo, Chen | - |
| dc.date.accessioned | 2023-03-30T03:02:11Z | - |
| dc.date.available | 2023-03-30T03:02:11Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s41095-022-0282-8 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7316 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | We present a lightweight and efficient semi-supervised video object segmentation network based on the space-time memory framework. To some extent, our method solves the two difficulties encountered in traditional video object segmentation: one is that the single frame calculation time is too long, and the other is that the current frame’s segmentation should use more information from past frames. The algorithm uses a global context (GC) module to achieve high-performance, real-time segmentation. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | global context | vi |
| dc.subject | memory framework | vi |
| dc.title | Global video object segmentation with spatial constraint module | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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