The paper introduces Referring Multi-Object Tracking (RMOT), a task that uses natural language expressions to guide the prediction of multiple objects over time, addressing limitations in previous work that lacked dynamic language expressiveness. It presents the Refer-KITTI-V2 benchmark, which includes 9,758 semi-automatically generated language prompts for diverse object dynamics. The authors also propose TempRMOT, a Transformer-based framework with a Temporal Enhancement Module that achieves state‑of‑the‑art results on both Refer‑KITTI and Refer‑KITTI‑V2.
By Yani Zhang, Dongming Wu, Wencheng Han, Xingping Dong, Shengcai Liao, Bo Du
arXiv:2606. 06853v1 Announce Type: cross Abstract: The new era has witnessed a remarkable capability to extend Vision-Language Models (VLMs) for tackling tasks of video understanding.
By Yifan Xu, Chao Zhang, Ruifei Ma, Fei Gao, Zhifei Yang, Jiaxing Qi, Zhipeng Chen
arXiv:2608.29126v1 Announce Type: new
Abstract: Referring single-object tracking enables language-grounded target initialization and subsequent tracking by jointly leveraging semantic cues and visual...
By Han Wang, Yuxuan Liu, Yuhan Sun, Jian Yang, Xiaotong Xu, Yixuan Lv, Zhuang Zhou, Shengyang Li
The paper introduces ORMOT, a new task that extends Referring Multi‑Object Tracking to omnidirectional 360° imagery, ensuring full scene context for language‑guided tracking. It presents ORSet, a dataset of 27 omnidirectional scenes with 848 language descriptions and 3,401 annotated objects, and introduces ORTrack, an LVLM‑driven framework that performs zero‑shot detection and robust cross‑frame association. Experiments on ORSet show that ORTrack achieves state‑of‑the‑art performance, establishing a strong baseline for future research.
By Zihan Zhou, Sijia Chen, Yanqiu Yu, En Yu, Wenbing Tao
arXiv:2603. 24016v2 Announce Type: replace-cross Abstract: Multi-Object Tracking (MOT) has traditionally focused on a few specific categories, restricting its applicability to real-world scenarios involving diverse objects.
By Zekun Qian, Wei Feng, Ruize Han, Junhui Hou
YesTrack introduces a two‑stage referring multi‑object tracking approach that treats the task as a discriminative Yes/No verification problem, directly using multimodal large language models (MLLMs) without generating captions. It adds lightweight temporal consistency constraints—Temporal Confidence Prior (TCP) and Temporal Reference Propagation (TRP)—to improve reliability and efficiency. The method also extends to generic multi‑object tracking as YesTrack‑MOT, achieving state‑of‑the‑art performance on Refer‑KITTI datasets while remaining efficient even with the smallest Qwen3‑VL variant.
By Quansheng Hu, Qin Sun, Qiansen Dai, Jin Ding, Wan Zhang, Xue Zhou, Jianxiao Zou
The paper introduces DRMOT, a new dataset and framework for Referring Multi-Object Tracking (RMOT) that incorporates RGB-D data. DRSet contains 2,076 referring expressions across 186 RGB-D sequences, pairing synchronized RGB images, depth maps, and language descriptions—including depth-dependent cues—to evaluate spatial‑semantic detection and tracking. The proposed DRTrack framework uses a Geometric‑Aware GRPO fine‑tuned Multimodal Large Language Model to perform precise detection and robust trajectory association from joint language, RGB, and depth inputs, with experiments demonstrating its effectiveness.
By Sijia Chen, Shaofeng Liang, Lijuan Ma, Yanqiu Yu, Nan Li, Wenshuo Chen, Runwei Guan, Dongming Wu, En Yu, Yutao Yue
arXiv:2603. 22282v2 Announce Type: replace-cross Abstract: We present UniMotion, to our knowledge the first unified framework for simultaneous understanding and generation of human motion, natural language, and RGB images within a single architecture.
By Ziyi Wang, Xinshun Wang, Shuang Chen, Yang Cong, Mengyuan Liu
Referring Video Object Segmentation (RVOS) aims to segment referred objects at the pixel level in video sequences based on natural language descriptions. Existing methods typically introduce motion information within a unified cross-modal temporal modeling framework, where language cues are used for target localization and segmentation.
arXiv:2602.01257v2 Announce Type: replace
Abstract: Recently, point-supervised temporal action localization has gained significant attention for its effective balance between labeling costs and local...
By Yunchuan Ma, Laiyun Qing, Guorong Li, Yuqing Liu, Yuankai Qi, Qingming Huang
ESMTrack is a fully end‑to‑end self‑supervised RGB‑T tracking framework that eliminates the need for costly modality‑aligned bounding boxes or offline pseudo‑label generation. It learns discriminative, temporally consistent representations using a grounding triplet loss on the initial annotated frame and a cross‑frame temporal triplet loss on unlabeled search frames, with reliable samples selected via forward‑backward consistency. A three‑branch architecture (fusion, RGB, thermal) and a modality decoupling mechanism mitigate modality dominance bias, enabling competitive state‑of‑the‑art performance, strong cross‑dataset generalization, and real‑time inference on five RGB‑T benchmarks.
By Shenglan Li, Rui Yao, Kunyang Sun, Hong Jia, Yong Zhou, Javen Qinfeng Shi, Xinyu Zhang
The paper introduces TSDA-Track, a Template-Search Domain Adaptation framework designed to reduce modality gaps in cross‑modal visual object tracking. Two variants are explored: Pre‑AFA TSDA‑Track uses adversarial alignment before transformer interaction, while Enc‑CFA TSDA‑Track applies contrastive alignment after interaction to strengthen cross‑modal correspondence. Experiments on datasets such as LasHeR, RGBT234, GTOT, and Anti‑UAV‑024 show that both variants outperform state‑of‑the‑art trackers, with Pre‑AFA achieving an SR/PR of 43.2/56.0 on RGBT234 under the modality‑switch protocol.
By Fereshteh Aghaee Meibodi, Amir Mehdi Soufi Enayati, Shadi Alijani, Homayoun Najjaran