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Putting the Object Back into Video Object Segmentation

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arxiv 2310.12982 v2 pith:NLZ3O65F submitted 2023-10-19 cs.CV

Ho Kei Cheng , Seoung Wug Oh , Brian Price , Joon-Young Lee , Alexander Schwing This is my paper · ORCID
classification cs.CV
keywords objectcutiememorysegmentationreadingvideobackbottom-up
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. FRAME: Pre-Training Video Feature Representations via Anticipation and Memory

    cs.CV 2025-06 conditional novelty 6.0 of 10

    FRAME distills DINO and CLIP features into a compact video encoder with a memory module and future-frame prediction, outperforming image-based and self-supervised video baselines on dense video tasks.

  2. ACE: Agentic Control for Embodied Manipulation via Zero-shot Workflow Reasoning

    cs.RO 2026-07 conditional novelty 5.0 of 10

    ACE decouples LLM workflow reasoning from a mask-conditioned pick-and-place policy, reaching 50–70% success on zero-shot complex tabletop tasks where end-to-end baselines score 0%.

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