面向4D高斯表示中复杂物体分割的查询重写
Query Rewriting for Complex Object Segmentation in 4D Gaussian Representations
- University of Science（科学大学）
- Viet Nam National University, Ho Chi Minh City（越南国家大学胡志明市分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对4D高斯表示中复杂物体分割任务的查询噪声问题，提出无需训练的关键词锚定查询重写策略，在HyperNeRF和Neu3D上实现了时间定位与空间分割性能的显著提升。
AI中文摘要：
近期4D高斯表示框架在语言引导的动态场景理解中展现出优异性能，但这类方法对包含冗余上下文信息的冗长叙事式查询仍十分敏感。本文探究了4D高斯表示中复杂物体分割任务下查询重写的影响，受检索增强语言模型及关键词引导查询重构的最新研究成果启发，提出一种无需训练的重新解释策略，将长描述性查询转换为简洁的关键词锚定形式，逐步减少语言噪声的同时保留与以物体为中心的表示相关的语义锚点。在HyperNeRF和Neu3D上开展的实验表明，简洁的重写查询可显著提升时间定位与空间分割性能，具体而言，本文方法无需额外微调，即可将平均时间准确率从60.92%提升至92.21%，平均vIoU从20.08%提升至76.94%；大量消融研究进一步揭示，更短的关键词聚焦查询可产生稳定的视频特征相似度分布，并与以物体为中心的高斯表示实现更好的对齐。
英文摘要：
Recent 4D Gaussian representation frameworks have demonstrated strong performance in language-guided dynamic scene understanding. However, these methods remain highly sensitive to verbose and narrative-style queries that contain noisy contextual information. In this paper, we investigate the impact of query rewriting for complex object segmentation in 4D Gaussian representations. Inspired by recent findings in retrieval-augmented language models and keyword-guided query reformulation, we propose a training-free reinterpretation strategy that transforms long descriptive queries into concise keyword-grounded forms. Our approach progressively reduces linguistic noise while preserving semantic anchors relevant to object-centric representations. Experiments on HyperNeRF and Neu3D demonstrate that concise rewritten queries significantly improve both temporal localization and spatial segmentation performance. In particular, our method improves average temporal accuracy from 60.92% to 92.21% and average vIoU from 20.08% to 76.94% without any additional fine-tuning. Extensive ablation studies further reveal that shorter, keyword-focused queries consistently yield stable video-feature similarity distributions and better alignment with object-centric Gaussian representations
