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New SA-VIS method trains video segmentation with sparse annotations

By PulseAugur Editorial · [2 sources] ·

Researchers have developed SA-VIS, a novel method for training video instance segmentation (VIS) models using sparse frame annotations. This approach significantly reduces the computational cost and annotation effort associated with traditional VIS training, which typically requires densely annotated frames. SA-VIS employs a Past-frames Feature Propagation module and light-weight Instance Queries to effectively learn from sparse labels, bridging the accuracy gap between sparse and dense annotation methods with minimal performance loss. AI

IMPACT Reduces annotation costs and computational requirements for video instance segmentation, potentially accelerating research and application development in the field.

RANK_REASON The cluster contains a research paper detailing a new method for video instance segmentation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New SA-VIS method trains video segmentation with sparse annotations

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The cluster contains a research paper detailing a new method for video instance segmentation.
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2 independent sources
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97 days old
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Edoardo Mello Rella, Ajad Chhatkuli, Shipra Jain, Ender Konukoglu, Luc Van Gool ·

    SA-VIS: Sparse frame Annotations for training Video Instance Segmentation

    arXiv:2606.20140v1 Announce Type: new Abstract: Recent online video instance segmentation (VIS) methods have achieved impressive results, thus becoming the preferred approach to segment instances in videos. Despite the resurgence of impressive single image models, the online (or …

  2. arXiv cs.CV TIER_1 English(EN) · Luc Van Gool ·

    SA-VIS: Sparse frame Annotations for training Video Instance Segmentation

    Recent online video instance segmentation (VIS) methods have achieved impressive results, thus becoming the preferred approach to segment instances in videos. Despite the resurgence of impressive single image models, the online (or semi-online) VIS approaches outperform single-im…

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