告诉我要跟踪什么：注入鲁棒语言引导以增强指代表多目标跟踪
Tell Me What to Track: Infusing Robust Language Guidance for Enhanced Referring Multi-Object Tracking
AI总结：
针对 RMOT 中新生目标数据不平衡及语言引导融合不足的问题，提出协同匹配、跨模态多尺度增强和查询显式指代表引导，性能提升 3.42%。
摘要：
指代表多目标跟踪（RMOT）是一项新兴的跨模态任务，旨在根据语言表达定位任意数量的目标，并在视频中持续跟踪它们。这项复杂任务涉及对多模态数据进行推理，以及结合时间关联实现精确的目标定位。然而，由于该任务的固有特性，以往研究忽视了新生目标与已有目标之间不平衡的数据分布。此外，它们仅对多模态特征进行间接融合，难以为新生目标检测提供清晰引导。为解决上述问题，我们采用协同匹配策略来缓解这种不平衡带来的影响，在保持跟踪性能的同时提升检测新生目标的能力。在编码器中，我们整合并增强了跨模态与多尺度融合，克服了以往工作中特征图之间共享和交互的多模态信息有限的瓶颈。在解码器中，我们还开发了一种指代表注入适配模块，通过查询 token 提供显式的指代表引导。实验表明，与以往工作相比，我们的模型取得了更优性能（+3.42%），证明了所提设计的有效性。
英文摘要：
Referring multi-object tracking (RMOT) is an emerging cross-modal task that aims to localize an arbitrary number of targets based on a language expression and continuously track them in a video. This intricate task involves reasoning on multi-modal data and precise target localization with temporal association. However, prior studies overlook the imbalanced data distribution between newborn targets and existing targets due to the nature of the task. In addition, they only indirectly fuse multi-modal features, struggling to deliver clear guidance on newborn target detection. To solve the above issues, we conduct a collaborative matching strategy to alleviate the impact of the imbalance, boosting the ability to detect newborn targets while maintaining tracking performance. In the encoder, we integrate and enhance the cross-modal and multi-scale fusion, overcoming the bottlenecks in previous work, where limited multi-modal information is shared and interacted between feature maps. In the decoder, we also develop a referring-infused adaptation that provides explicit referring guidance through the query tokens. The experiments showcase the superior performance of our model (+3.42%) compared to prior works, demonstrating the effectiveness of our designs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
