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LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation

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arxiv 2404.19326 v2 pith:WUWCZJ32 submitted 2024-04-30 cs.CV

Lingyi Hong , Zhongying Liu , Wenchao Chen , Chenzhi Tan , Yuang Feng , Xinyu Zhou , Pinxue Guo , Jinglun Li
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Zhaoyu Chen Shuyong Gao Wei Zhang Wenqiang Zhang
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classification cs.CV
keywords lvosvideomodelsvideosbenchmarksexistinglong-termobjects
verification ladder T0 review T1 audit T2 compute T3 formal
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Video object segmentation (VOS) aims to distinguish and track target objects in a video. Despite the excellent performance achieved by off-the-shell VOS models, existing VOS benchmarks mainly focus on short-term videos lasting about 5 seconds, where objects remain visible most of the time. However, these benchmarks poorly represent 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, approximately 5 times longer than videos in existing datasets. Each video includes various attributes, especially challenges deriving from 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 20 existing VOS models under 4 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 key factor to accuracy decline is the increased video length, emphasizing LVOS's crucial role. We hope our LVOS can advance development of VOS in real scenes. Data and code are available at https://lingyihongfd.github.io/lvos.github.io/.

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Cited by 7 Pith papers

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