CD-RMOT-Bench: Benchmarking the Cross-Domain Referring Multi-Object Tracking

• URL: http://arxiv.org/abs/2607.25239v1
• Date: Tue, 28 Jul 2026 03:29:19 GMT
• Title: CD-RMOT-Bench: Benchmarking the Cross-Domain Referring Multi-Object Tracking
• Abstract summary: Cross-Domain Referring Multi-Object Tracking (CD-RMOT) is a new problem that evaluates whether an RMOT model can reliably follow natural-language expressions in an unlabeled target domain with different visual conditions.<n>We construct CD-RMOT-Bench, a unified benchmark that combines real clear-domain referring tracking data, aligned digital-twin variants, and real adverse-domain videos.<n>Extensive experiments reveal that domain shifts severely degrade RMOT performance, where the failure is not merely caused by object detection errors but more critically by unstable expression-conditioned temporal association and target selection.
• Score: 18.30427236092477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Referring multi-object tracking (RMOT) extends tracking from category-driven perception to language-guided understanding by grounding object trajectories in natural-language expressions. Despite recent progress, existing RMOT studies are largely conducted under in-domain settings, leaving the robustness of language-conditioned tracking under inevitable visual domain shifts unexplored. In this paper, we study Cross-Domain Referring Multi-Object Tracking (CD-RMOT), a new and challenging problem that evaluates whether an RMOT model trained on a labeled source domain can reliably follow natural-language expressions in an unlabeled target domain with different visual conditions. To support systematic study, we construct CD-RMOT-Bench, a unified benchmark that combines real clear-domain referring tracking data, aligned digital-twin variants, and real adverse-domain videos. CD-RMOT-Bench enables both controlled weather/viewpoint shift analysis and realistic synthetic-real transfer evaluation under a shared RMOT protocol. Further, we provide a Query-Centric Adaptation (QCA) framework, designed to stabilize the query space that bridges visual trajectories and referring expressions. Extensive experiments reveal that domain shifts severely degrade RMOT performance, where the failure is not merely caused by object detection errors but more critically by unstable expression-conditioned temporal association and target selection. QCA establishes a strong baseline, while CD-RMOT-Bench opens a new direction for robust language-guided tracking across visual domains.

Related papers

• VPRef: A Cross-Domain Benchmark for Referring Remote Sensing Image Segmentation [17.658559673931965]
  This paper establishes the first cross-domain RRSIS benchmark, comprising 46,972 language-image-annotation triplets.<n>We develop a tailored parameter-efficient domain adaptation baseline anchored on the Segment Anything Model (SAM3) via Low-Rank Adaptation (LoRA)<n>Our framework counteracts visual distribution discrepancies through pseudo-label-driven self-training and addresses textual logic drift via random multi-granularity text prompt mixing.
  arXiv  Detail & Related papers  (2026-09-15T01:18:06Z)
• Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models [154.39583176906893]
  Large language models (MLs) have advanced understanding, they remain highly prone to hallucinations in scenes.<n>We argue this stems from a failure intemporal monitoring, the ability to persistently track object dynamic identities, states, and relations over time.<n>Existing benchmarks obscure this deficit relying on single final-answer queries that can often be resolved via local visual cues or statistical priors.
  arXiv  Detail & Related papers  (2026-05-09T14:32:36Z)
• VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection [33.39250067795076]
  In real-world scenarios, continual changes in weather, illumination, and imaging conditions cause significant domain shifts.<n>We propose a dual-prior learning framework for generalized object detection (SDGOD)<n>In the encoding stage, we propose Cross-domain Stable Prior Distillation to enhance the robustness of object-background and inter-instance modeling.<n>In the decoding stage, we propose Semantic-Contextual Prior-based Query Enhancement.
  arXiv  Detail & Related papers  (2026-04-23T10:04:36Z)
• RASR: Retrieval-Augmented Semantic Reasoning for Fake News Video Detection [38.069086560563285]
  Multimodal fake news video detection is a crucial research direction for maintaining the credibility of online information.<n>Existing studies primarily verify content authenticity by constructing multimodal feature fusion representations or utilizing pre-trained language models.<n>We propose a novel Retrieval-Augmented Semantic Reasoning (RASR) framework to overcome these limitations.
  arXiv  Detail & Related papers  (2026-04-08T05:03:34Z)
• RAGTrack: Language-aware RGBT Tracking with Retrieval-Augmented Generation [71.2136732268131]
  RGB-Thermal (RGBT) tracking aims to achieve robust object localization across diverse environmental conditions.<n>Existing RGBT trackers rely solely on initial-frame visual information for target modeling.<n>We propose RAGTrack, a novel Retrieval-Augmented Generation framework for robust RGBT tracking.
  arXiv  Detail & Related papers  (2026-03-04T01:02:04Z)
• Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection [21.137567686181438]
  ours is a self-supervised framework that emphvisually probes target domains without labels.<n>ours consistently outperforms strong supervised, self-supervised, and adaptation baselines.<n>These results highlight self-supervised prompting as a practical direction for building scalable and adaptive visual inspection systems.
  arXiv  Detail & Related papers  (2025-11-16T01:28:45Z)
• See No Evil: Adversarial Attacks Against Linguistic-Visual Association in Referring Multi-Object Tracking Systems [21.34084466103555]
  We present VEIL, a novel adversarial framework designed to disrupt the unified referring-matching mechanisms of RMOT models.<n>We show that carefully crafted digital and physical perturbations can corrupt the tracking logic reliability, inducing track ID switches and terminations.
  arXiv  Detail & Related papers  (2025-09-02T07:17:32Z)
• Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector [72.05791402494727]
  This paper studies the challenging cross-domain few-shot object detection (CD-FSOD) It aims to develop an accurate object detector for novel domains with minimal labeled examples.
  arXiv  Detail & Related papers  (2024-02-05T15:25:32Z)
• Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing [72.14449502499535]
  The task of analyzing the internal rhetorical structure of texts is a challenging problem in natural language processing. Despite the recent advances in neural models, the lack of large-scale, high-quality corpora for training remains a major obstacle. Recent studies have attempted to overcome this limitation by using distant supervision.
  arXiv  Detail & Related papers  (2023-05-23T07:13:51Z)
• Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency [90.71745178767203]
  Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets. Existing 3D domain adaptive detection methods often assume prior access to the target domain annotations, which is rarely feasible in the real world. We study a more realistic setting, unsupervised 3D domain adaptive detection, which only utilizes source domain annotations.
  arXiv  Detail & Related papers  (2021-07-23T17:19:23Z)
• Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID [62.90727103061876]
  Unsupervised domain adaptation (UDA) aims at adapting the model trained on a labeled source-domain dataset to an unlabeled target-domain dataset. We propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term. Our proposed framework is shown to achieve state-of-the-art performance on multiple UDA tasks of person re-ID.
  arXiv  Detail & Related papers  (2020-03-14T14:45:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.