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  1. Home
  3. Crisp: Contrastive Residual Injection And Semantic Prompting For Continual Video Instance Segmentation.
  1. Home
  3. Crisp: Contrastive Residual Injection And Semantic Prompting For Continual Video Instance Segmentation.

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CRISP: Contrastive Residual Injection and Semantic Prompting for Continual Video Instance Segmentation.

Baichen Liu, Qi Lyu, Xudong Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 12, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces CRISP, a novel framework for continual video instance segmentation (CVIS) that enhances plasticity and stability. CRISP effectively addresses instance, category, and task confusion, significantly improving segmentation and classification performance in long-term learning scenarios.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Continual video instance segmentation (CVIS) demands models that can learn new categories without forgetting previous knowledge.
    • Maintaining temporal consistency of instances across video frames is a critical challenge in CVIS.
    • Existing methods struggle with instance-wise, category-wise, and task-wise confusion during incremental learning.

    Purpose of the Study:

    • To introduce a new framework, Contrastive Residual Injection and Semantic Prompting (CRISP), to tackle the complexities of CVIS.
    • To enhance both plasticity (learning new categories) and stability (retaining old knowledge) in CVIS models.
    • To improve temporal consistency and reduce catastrophic forgetting in long-term CVIS tasks.

    Main Methods:

  • CRISP employs instance tracking and an instance correlation loss for instance-wise learning, focusing on query space correlation and task specificity.
  • An adaptive residual semantic prompt (ARSP) learning framework with a query-prompt matching mechanism is used for category-wise learning.
  • Contrastive learning and a semantic consistency loss maintain semantic coherence, while a prompt initialization strategy addresses task-wise learning.

    • Main Results:

    • CRISP significantly outperforms existing continual segmentation methods on YouTube-VIS-2019 and YouTube-VIS-2021 datasets.
    • The framework effectively avoids catastrophic forgetting, a common issue in continual learning.
    • Demonstrated improvements in both segmentation and classification performance in long-term CVIS.

    Conclusions:

    • CRISP provides an effective solution for the challenges in continual video instance segmentation.
    • The proposed methods successfully balance plasticity and stability, crucial for incremental learning.
    • CRISP offers a promising direction for advancing long-term continual video instance segmentation research.