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250918s2025 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2025.3611020
|2 doi
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|a pubmed25n1573.xml
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
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|a Hong, Lingyi
|e verfasserin
|4 aut
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| 245 |
1 |
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|a LVOS
|b A Benchmark for Large-Scale Long-Term Video Object Segmentation
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| 264 |
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|c 2025
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| 336 |
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|a Text
|b txt
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|a Date Revised 17.09.2025
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Video object segmentation (VOS) aims to distinguish and track target objects in a video. Despite the excellent performance achieved by off-the-shelf VOS models, part of the existing VOS benchmarks mainly focuses on short-term videos, where objects remain visible most of the time. However, these benchmarks may not fully capture challenges encountered in practical applications, and the absence of long-term datasets restricts further investigation of VOS in realistic scenarios. Thus, we propose a novel benchmark named LVOS, comprising 720 videos with 296,401 frames and 407,945 high-quality annotations. Videos in LVOS last 1.14 minutes on average. Each video includes various attributes, especially challenges encountered in the wild, such as long-term reappearing and cross-temporal similar objects. Compared to previous benchmarks, our LVOS better reflects VOS models' performance in real scenarios. Based on LVOS, we evaluate 15 existing VOS models under 3 different settings and conduct a comprehensive analysis. On LVOS, these models suffer a large performance drop, highlighting the challenge of achieving precise tracking and segmentation in real-world scenarios. Attribute-based analysis indicates that one of the significant factors contributing to accuracy decline is the increased video length, interacting with complex challenges such as long-term reappearance, cross-temporal confusion, and occlusion, which emphasize LVOS's crucial role. We hope our LVOS can advance development of VOS in real scenes
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|a Journal Article
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|a Liu, Zhongying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Wenchao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tan, Chenzhi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Feng, Yuang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Xinyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Pinxue
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Jinglun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Zhaoyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Shuyong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Wenqiang
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g PP(2025) vom: 17. Sept.
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
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|g volume:PP
|g year:2025
|g day:17
|g month:09
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|u http://dx.doi.org/10.1109/TPAMI.2025.3611020
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