2026-07-02 04:00 UTCOriginal source2 min readUpdated: 2026-07-02 08:20 UTC

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

This paper introduces SegFS, a dual-stream fast-slow framework for open-vocabulary video instance segmentation (OV-VIS). By using a slow, accurate path on sparse keyframes and a fast, lightweight path on subsequent frames, it achieves up to 14x lower latency than the mobile-oriented MOBIUS model while maintaining competitive accuracy.

SourcearXiv Computer VisionAuthor: Luca Barsellotti, Martin Sundermeyer, Mattia Segu, Nikita Araslanov, Muhammad Ferjad Naeem, Marcella Cornia, Yongqin Xian, Maxim Berman

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[Submitted on 30 Jun 2026]

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

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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.

Comments: ECCV 2026

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.00124 [cs.CV]

(or arXiv:2607.00124v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.00124

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Luca Barsellotti [view email] [v1] Tue, 30 Jun 2026 19:59:19 UTC (13,117 KB)

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