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
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:2510. 14904v4 Announce Type: replace-cross Abstract: Dense Video Object Captioning (DVOC) is the task of jointly detecting, tracking, and captioning object trajectories in a video, requiring the ability to understand spatio-temporal details and describe them in natural language.
By Gabriel Fiastre, Antoine Yang, Cordelia Schmid
arXiv:2511.17681v2 Announce Type: replace
Abstract: Referring Multi-Object Tracking (RMOT) extends conventional multi-object tracking (MOT) by introducing natural language references for multi-modal...
By Weiyi Lv, Ning Zhang, Hanyang Sun, Haoran Jiang, Kai Zhao, Yixiao Gu, Jing Xiao, Dan Zeng
arXiv:2606. 29357v1 Announce Type: cross Abstract: Vision-language tracking guided by natural language specifications leverages high-level semantic cues of target objects to substantially boost tracking accuracy and robustness.
By Xiao Wang, Liye Jin, Dan Xu, Yuehang Li, Lan Chen, Yaowei Wang, Yonghong Tian, Jin Tang
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 FindIt, the first comprehensive benchmark for evaluating the promptable localization abilities of generalist multimodal large language models (MLLMs). It covers four core task categories—object detection, referring expression detection, instance-level detection, and video-based detection—and provides a unified framework that standardizes inputs, enforces parsable bounding box outputs, and defines transparent evaluation protocols. Using this benchmark, the authors assess a range of open-source and proprietary MLLMs, revealing both their strengths and limitations, particularly their sensitivity to formatting constraints and difficulty generalizing to minor variations.
By Eshika Khandelwal, Jingjing Pan, Mingfang Zhang, Quan Kong, Lorenzo Garattoni, Hilde Kuehne
The paper introduces OpenRef, a benchmark for Referring Expression Comprehension (REC) designed for open‑world scenarios. OpenRef expands beyond simple settings by including diverse visual domains, variable target counts (multi‑target and none‑target), and a rich vocabulary with proper nouns, polysemous words, and ordinal terms. It also proposes new evaluation metrics—F1 for grounding accuracy and N3R for negative expression rejection—and presents a training‑free Multi‑task Consistency Checker (MCC) that improves model performance with a single click.
By Zongjian Wu, Lei Zhang
VidOmni-Bench is a new benchmark for fine‑grained video understanding that asks models to verify whether each event in dense video captions is supported by the video. It contains 500 videos covering five complexity types and durations from 4 seconds to 90 minutes, and uses human‑verified sentence‑level labels to create hard negatives. Experiments show that Video‑LLMs often hallucinate events, struggle to detect incorrect descriptions, and exhibit varying weaknesses depending on video complexity and duration.
By Changbeen Kim, Junwon Chang, Kipyo Kim, Risa Shinoda, Kuniaki Saito, Donghyun Kim
STRAND is a new benchmark that tests multimodal large language models’ ability to track objects, their states, and relationships over time in videos. It evaluates intermediate reasoning by breaking queries into sub‑questions and uses Faithful Accuracy to ensure all parts of an answer are correct. The authors also propose an object‑centric framework that builds structured trajectories and shows reduced hallucinations and better temporal consistency compared to existing models.
By Thong Nguyen, Tri Cao, Khoi Le, Cong-Duy Nguyen, Quynh Vo, See-Kiong Ng, Bryan Hooi Kuen-Yew
arXiv:2601. 06550v3 Announce Type: replace-cross Abstract: Semantic Multi-Object Tracking (SMOT) is evolving from purely geometric localization toward comprehensive video understanding.
By Pan Liao, Feng Yang, Di Wu, Jinwen Yu, Wang Zhao, Dingwen Zhang
arXiv:2501.04001v4 Announce Type: replace
Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-...
By Haobo Yuan, Xiangtai Li, Tao Zhang, Yueyi Sun, Zilong Huang, Shilin Xu, Shunping Ji, Yunhai Tong, Lu Qi, Jiashi Feng, Ming-Hsuan Yang