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Segmenting, Fast and Slow: Real-Time Open-Vocabulary Video Instance Segmentation With Dual-Path Processing

MCML Authors

Abstract

Object-centric models inspired by DETR have become the dominant paradigm for open-vocabulary video instance segmentation (OV-VIS). While recent efforts have reduced the computational cost of pixel decoding, textual modality fusion, and object decoding to make these architectures more suitable for mobile devices, real-time on-device inference at high frame rates remains an open challenge. In this paper, we introduce SegFS, a dual-stream fast-slow framework that significantly improves efficiency without sacrificing accuracy. On sparse keyframes, an open-vocabulary object-based model predicts instance-level representations. These representations are then projected back into the backbone feature space to condition a lightweight fast network, which efficiently relocalizes and segments the instances in subsequent frames. By shifting instance propagation from object decoding to feature-space conditioning, our approach decouples multimodal semantic understanding from dense mask prediction and enables efficient temporal propagation. The proposed fast branch achieves up to 14x lower latency than the mobile-oriented MOBIUS model, while maintaining competitive segmentation performance on standard OV-VIS benchmarks.

inproceedings BSS+26

ECCV 2026

19th European Conference on Computer Vision. Malmö, Sweden, Sep 08-12, 2026. To be published. Preprint available.
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Authors

L. Barsellotti • M. Sundermeyer • M. Segu • N. Araslanov • M. Naeem • M. Cornia • Y. Xian • M. Berman

Links

arXiv

Research Area

 B1 | Computer Vision

BibTeXKey: BSS+26

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