MiNQVIS: Mitigating Noisy Queries for Robust Online Video Instance Segmentation

Online Video Instance Segmentation (VIS) relies on per-frame instance queries for causal detection, segmentation, and tempo-ral association. However, in realistic videos with occlusion, blur, andclutter, these predictions often become noisy—exhibiting false positives,missed instances, or inaccurate masks. Such noisy queries not only desta-bilize cross-frame association but also contaminate contrastive supervi-sion and propagate drift through memory-based matching. Despite ad-vances in recent online VIS methods, none explicitly identify or addressnoisy query contamination, leaving a fundamental weakness unexam-ined. We introduce MiNQVIS, the first noise-aware framework for on-line VIS that stabilizes both training and inference. Our approach con-sists of three complementary components: (1) query-level gating that fil-ters unreliable predictions using classification-guided thresholding andembedding-norm stabilization; (2) video-level prototypical contrastivelearning that aggregates only reliable matches into stable, noise-robustprototypes; (3) dual-memory association that fuses momentum embed-dings with history-best retrieval to prevent drift from corrupted frames.Experiments on YouTubeVIS 2019/2021 and OVIS show consistent im-provements over strong online VIS baselines, with large gains in challeng-ing scenes. Project Page: https://nothing898.github.io/MiNQVIS.