ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations

• URL: http://arxiv.org/abs/2501.14607v2
• Date: Mon, 30 Jun 2025 16:09:19 GMT
• Title: ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations
• Authors: Tianming Liang, Kun-Yu Lin, Chaolei Tan, Jianguo Zhang, Wei-Shi Zheng, Jian-Fang Hu,
• Abstract summary: Video object segmentation (RVOS) aims to segment target objects throughout a video based on a text description.<n>This is challenging as it involves deep vision-level understanding, pixel-level dense prediction andtemporal reasoning.<n>We propose bfReferDINO RVOS that inherits region-level vision-text alignment from foundational visual grounding models.
• Score: 33.74746234704817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Referring video object segmentation (RVOS) aims to segment target objects throughout a video based on a text description. This is challenging as it involves deep vision-language understanding, pixel-level dense prediction and spatiotemporal reasoning. Despite notable progress in recent years, existing methods still exhibit a noticeable gap when considering all these aspects. In this work, we propose \textbf{ReferDINO}, a strong RVOS model that inherits region-level vision-language alignment from foundational visual grounding models, and is further endowed with pixel-level dense perception and cross-modal spatiotemporal reasoning. In detail, ReferDINO integrates two key components: 1) a grounding-guided deformable mask decoder that utilizes location prediction to progressively guide mask prediction through differentiable deformation mechanisms; 2) an object-consistent temporal enhancer that injects pretrained time-varying text features into inter-frame interaction to capture object-aware dynamic changes. Moreover, a confidence-aware query pruning strategy is designed to accelerate object decoding without compromising model performance. Extensive experimental results on five benchmarks demonstrate that our ReferDINO significantly outperforms previous methods (e.g., +3.9% (\mathcal{J}&\mathcal{F}) on Ref-YouTube-VOS) with real-time inference speed (51 FPS).

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