提出FlexHook框架，让两阶段指代跟踪重回顶尖水平
Rethinking Two-Stage Referring-by-Tracking in Referring Multi-Object Tracking: Make it Strong Again
- 用采样+语言引导重构特征，替代盲目拼接
- 新匹配机制让跨模态对应更稳定，准确率显著提升
- 适合资源有限但需持续更新的部署场景
指代多目标跟踪（RMOT）旨在视频中追踪自然语言描述的多个目标。尽管单阶段方法取得显著进展，两阶段指代跟踪（RBT）因训练成本低、可增量部署仍具不可替代性。本文重新审视现有两阶段RBT框架，发现其存在特征构建过度依赖经验、对应关系建模脆弱两大缺陷。为此提出FlexHook：通过采样式策略与语言条件注入的条件钩子（C-Hook）重构特征；引入成对对应解码器（PCD），以主动建模替代CLIP相似度匹配，实现更灵活鲁棒的对应关系学习。在Refer-KITTI/v2、Refer-Dance和LaMOT等多个基准上实验表明，FlexHook是首个全面超越当前最先进方法的两阶段RBT方案。代码见https://github.com/buptLwz/FlexHook。
原文摘要 · Abstract (English)
Referring Multi-Object Tracking (RMOT) aims to track multiple objects specified by natural language expressions in videos. With the recent significant progress of one-stage methods, the two-stage Referring-by-Tracking (RBT) paradigm has gradually lost its popularity. However, its lower training cost and flexible incremental deployment remain irreplaceable. Rethinking existing two-stage RBT frameworks, we identify two fundamental limitations: the overly heuristic feature construction and fragile correspondence modeling. To address these issues, we propose FlexHook, a novel two-stage RBT framework. In FlexHook, the proposed Conditioning Hook (C-Hook) redefines the feature construction by a sampling-based strategy and language-conditioned cue injection. Then, we introduce a Pairwise Correspondence Decoder (PCD) that replaces CLIP-based similarity matching with active correspondence modeling, yielding a more flexible and robust strategy. Extensive experiments on multiple benchmarks (Refer-KITTI/v2, Refer-Dance, and LaMOT) demonstrate that FlexHook becomes the first two-stage RBT approach to comprehensively outperform current state-of-the-art methods. Code can be found in the https://github.com/buptLwz/FlexHook.
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