Putting the Object Back into Video Object Segmentation
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Abstract (arXiv)
We present Cutie, a video object segmentation (VOS) network with object-level memory reading, which puts the object representation from memory back into the video object segmentation result. Recent works on VOS employ bottom-up pixel-level memory reading which struggles due to matching noise, especially in the presence of distractors, resulting in lower performance in more challenging data. In contrast, Cutie performs top-down object-level memory reading by adapting a small set of object queries. Via those, it interacts with the bottom-up pixel features iteratively with a query-based object transformer (qt, hence Cutie). The object queries act as a high-level summary of the target object, while high-resolution feature maps are retained for accurate segmentation. Together with foreground-background masked attention, Cutie cleanly separates the semantics of the foreground object from the background. On the challenging MOSE dataset, Cutie improves by 8.7 J&F over XMem with a similar running time and improves by 4.2 J&F over DeAOT while being three times faster. Code is available at: https://hkchengrex.github.io/Cutie
Key facts. Cited by 188 papers in the map (data through 14 Oct 2026). Builds on 78 of its 97 references that are in the map. First posted on arXiv 19 Oct 2023.
Builds on
78 of 97 references.
References · 78
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- 2023MOSE: A New Dataset for Video Object Segmentation in Complex Scenescited by 234
- 2023Track Anything: Segment Anything Meets Videoscited by 234
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- 2021Reviving Iterative Training with Mask Guidance for Interactive Segmentationcited by 163
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- 2021Occluded Video Instance Segmentation: A Benchmarkcited by 154
- 2016Learning Video Object Segmentation from Static Imagescited by 151
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- 2021Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusioncited by 129
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- 2020CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinementcited by 113
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- 2020Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinementcited by 101
- 2022In Defense of Online Models for Video Instance Segmentationcited by 97
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- 2022Towards Grand Unification of Object Trackingcited by 93
- 2019Towards High-Resolution Salient Object Detectioncited by 92
- 2018VideoMatch: Matching based Video Object Segmentationcited by 90
- 2020Learning What to Learn for Video Object Segmentationcited by 89
- 2019RVOS: End-to-End Recurrent Network for Video Object Segmentationcited by 88
- 2021Efficient Regional Memory Network for Video Object Segmentationcited by 86
- 2020Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integrationcited by 81
- 2022LVOS: A Benchmark for Long-term Video Object Segmentationcited by 77
- 2020Learning Fast and Robust Target Models for Video Object Segmentationcited by 76
- 2021Hierarchical Memory Matching Network for Video Object Segmentationcited by 72
- 2021SwiftNet: Real-time Video Object Segmentationcited by 67
50 most cited of 78 All 78 (JSON)
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