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Causal Prompts for Open-Vocabulary Video Instance Segmentation.

Rongkun Zheng, Lu Qi, Xi Chen

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 3, 2026
    PubMed
    Summary
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    We introduce CPOVIS, a framework enhancing open-vocabulary video instance segmentation by using causal prompts from past frames. This improves object detection and tracking for novel categories in videos.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Open-vocabulary video instance segmentation aims to detect, segment, and track objects, including unknown categories.
    • Current methods often fail to utilize temporal information from previous frames, hindering generalization in open-world scenarios.

    Purpose of the Study:

    • To propose CPOVIS, a novel framework that enhances temporal reasoning and semantic consistency for open-vocabulary video instance segmentation.
    • To leverage causal prompts dynamically propagated from historical frames to improve performance on unseen object categories.

    Main Methods:

    • CPOVIS utilizes a Mask2Former architecture with a CLIP backbone, incorporating PromptCLIP for cross-modal alignment.
    • Key innovations include a Visual Prompt Injector for spatial-temporal coherence and a Taxonomy Prompt Infuser for semantic consistency.

    Related Experiment Videos

  • A contrastive learning strategy and adaptation of Segment Anything Model (SAM2) are employed to boost segmentation and tracking capabilities.

    • Main Results:

    • CPOVIS achieves state-of-the-art performance on seven challenging open- and closed-vocabulary video segmentation benchmarks.
    • The framework significantly outperforms existing methods in detecting, segmenting, and tracking objects, especially novel categories.
    • Causal prompt propagation is demonstrated to be crucial for advancing video understanding in open-world settings.

    Conclusions:

    • CPOVIS effectively addresses the limitations of existing methods by incorporating causal temporal cues.
    • The proposed framework demonstrates robust open-world generalization capabilities for video instance segmentation.
    • This work highlights the importance of causal prompt propagation for improving video analysis and object recognition in dynamic, open-world environments.