arXiv Computer Vision

YesTrack: Referring Multi-Object Tracking via MLLM-based Yes/No Verification

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.

arXiv Computer Vision

Sep 4

ORMOT: A Dataset and Framework for Omnidirectional Referring Multi-Object Tracking

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

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arXiv AI

Jun 2

CaptionFormer: Unified Segmentation, Tracking, and Captioning for Spatio-Temporal Objects

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

computer-vision multimodal benchmarks

arXiv Computer Vision

Aug 25

Motion-Aware Vision-Reference Alignment for Referring Multi-Object Tracking

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

llms multimodal benchmarks safety

arXiv AI

Jun 30

Dynamic Parsing and Updating Natural Language Specification using VLMs for Robust Vision-Language Tracking

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

llms multimodal benchmarks safety

arXiv Computer Vision

Sep 1

Efficient Language-to-Vision Feature Injection for Referring Single-Object Tracking

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

llms multimodal benchmarks safety

arXiv Computer Vision

Sep 3

Towards Open-World Referring Expression Comprehension: A Benchmark with Training-free Multi-task Consistency Checker

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

llms robotics multimodal benchmarks

arXiv AI

Jul 7

Generative Semantic Multi-Object Tracking: A Large-Scale Benchmark and an MLLM-Driven Reasoning Framework

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

llms multimodal benchmarks safety

arXiv Computer Vision

Aug 25

Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos

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

llms computer-vision fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Aug 17

Unlocking Motion in Expressions: Temporal Calibration for Referring Video Object Segmentation

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.

computer-vision multimodal benchmarks

arXiv AI

Aug 18

Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models

arXiv:2605. 08974v2 Announce Type: replace-cross Abstract: While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes.

By Tri Cao, Khoi Le, Thong Nguyen, Cong-Duy Nguyen, Quynh Vo, Anh Tuan Luu, Chunyan Miao, See-Kiong Ng, Shuicheng Yan, Bryan Hooi

llms multimodal benchmarks safety

arXiv AI

Aug 28

Temporally-Grounded Language Generation: Towards Real-Time Vision-Language Models

The paper introduces Temporally-Grounded Language Generation (TGLG), a benchmark that tests vision‑language models on their ability to produce semantically accurate and temporally precise utterances in real‑time settings. It identifies perceptual updating and contingency awareness as key capabilities, curates datasets from sports broadcasting and egocentric interactions, and proposes the TRACE metric to jointly evaluate semantic similarity and temporal alignment. The authors also present VLM‑TSI, a model that interleaves visual and linguistic tokens in a time‑synchronized manner, achieving better performance than a strong baseline yet still showing modest overall results, underscoring the challenge of real‑time VLMs.

By Keunwoo Peter Yu, Joyce Chai

llms nlp multimodal benchmarks safety

arXiv Computer Vision

4d ago

VANTAGE-Bench: Evaluating the Infrastructure AI Gap in Vision-Language Models

arXiv:2609.09396v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric...

By Zaid Pervaiz Bhat, Nimra Nayyar, Arihant Jain, Lap Fung Chan, John Suchanek, Yu Wang, Varun Praveen, Tomasz Kornuta, Vidya Nariyambut Murali

llms nlp robotics multimodal benchmarks safety

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